R-universe search: counterfactual

dandls

counterfactuals:Counterfactual Explanations

Modular and unified R6-based interface for counterfactual explanation methods. The following methods are currently implemented: Burghmans et al. (2022) <doi:10.48550/arXiv.2104.07411>, Dandl et al. (2020) <doi:10.1007/978-3-030-58112-1_31> and Wexler et al. (2019) <doi:10.1109/TVCG.2019.2934619>. Optional extensions allow these methods to be applied to a variety of models and use cases. Once generated, the counterfactuals can be analyzed and visualized by provided functionalities.

Maintained by Susanne Dandl. Last updated 5 months ago.

interpretable-machine-learning local-explanations model-agnostic-explanations

72.2 match 21 stars 7.14 score 22 scripts

ivanfernandezval

Counterfactual:Estimation and Inference Methods for Counterfactual Analysis

Implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.

Maintained by Ivan Fernandez-Val. Last updated 5 years ago.

58.7 match 3 stars 2.48 score 10 scripts

santikka

cfid:Identification of Counterfactual Queries in Causal Models

Facilitates the identification of counterfactual queries in structural causal models via the ID* and IDC* algorithms by Shpitser, I. and Pearl, J. (2007, 2008) <arXiv:1206.5294>, <https://jmlr.org/papers/v9/shpitser08a.html>. Provides a simple interface for defining causal diagrams and counterfactual conjunctions. Construction of parallel worlds graphs and counterfactual graphs is carried out automatically based on the counterfactual query and the causal diagram. See Tikka, S. (2023) <doi:10.32614/RJ-2023-053> for a tutorial of the package.

Maintained by Santtu Tikka. Last updated 8 months ago.

causal-inference causal-models causality-algorithms counterfactual counterfactuals directed-acyclic-graph identifiability

30.1 match 7 stars 4.02 score 2 scripts 1 dependents

florianstijven

Surrogate:Evaluation of Surrogate Endpoints in Clinical Trials

In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine.

Maintained by Wim Van Der Elst. Last updated 19 days ago.

10.4 match 1 stars 6.42 score 133 scripts

iqss

WhatIf:Software for Evaluating Counterfactuals

Inferences about counterfactuals are essential for prediction, answering what if questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Unfortunately, standard statistical approaches assume the veracity of the model rather than revealing the degree of model-dependence, which makes this problem hard to detect. WhatIf offers easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models. If an analysis fails the tests offered here, then we know that substantive inferences will be sensitive to at least some modeling choices that are not based on empirical evidence, no matter what method of inference one chooses to use. WhatIf implements the methods for evaluating counterfactuals discussed in Gary King and Langche Zeng, 2006, "The Dangers of Extreme Counterfactuals," Political Analysis 14 (2) <DOI:10.1093/pan/mpj004>; and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The Pitfalls of Counterfactual Inference," International Studies Quarterly 51 (March) <DOI:10.1111/j.1468-2478.2007.00445.x>.

Maintained by Soubhik Barari. Last updated 2 years ago.

10.1 match 18 stars 5.80 score 14 scripts

mmukaigawara

geocausal:Causal Inference with Spatio-Temporal Data

Spatio-temporal causal inference based on point process data. You provide the raw data of locations and timings of treatment and outcome events, specify counterfactual scenarios, and the package estimates causal effects over specified spatial and temporal windows. See Papadogeorgou, et al. (2022) <doi:10.1111/rssb.12548> and Mukaigawara, et al. (2024) <doi:10.31219/osf.io/5kc6f>.

Maintained by Mitsuru Mukaigawara. Last updated 29 days ago.

8.3 match 45 stars 5.73 score

nhejazi

txshift:Efficient Estimation of the Causal Effects of Stochastic Interventions

Efficient estimation of the population-level causal effects of stochastic interventions on a continuous-valued exposure. Both one-step and targeted minimum loss estimators are implemented for the counterfactual mean value of an outcome of interest under an additive modified treatment policy, a stochastic intervention that may depend on the natural value of the exposure. To accommodate settings with outcome-dependent two-phase sampling, procedures incorporating inverse probability of censoring weighting are provided to facilitate the construction of inefficient and efficient one-step and targeted minimum loss estimators. The causal parameter and its estimation were first described by Díaz and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust estimation procedure and its application to data from two-phase sampling designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert, and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package implementation is described in NS Hejazi and DC Benkeser (2020) <doi:10.21105/joss.02447>. Estimation of nuisance parameters may be enhanced through the Super Learner ensemble model in 'sl3', available for download from GitHub using 'remotes::install_github("tlverse/sl3")'.

Maintained by Nima Hejazi. Last updated 6 months ago.

causal-effects causal-inference censored-data machine-learning robust-statistics statistics stochastic-interventions stochastic-treatment-regimes targeted-learning treatment-effects variable-importance

9.0 match 14 stars 5.12 score 19 scripts

osofr

simcausal:Simulating Longitudinal Data with Causal Inference Applications

A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples.

Maintained by Oleg Sofrygin. Last updated 8 months ago.

counterfactual-data sem simulated-network simulating-data structural-equations

7.5 match 67 stars 6.06 score 170 scripts

tlverse

tmle3:The Extensible TMLE Framework

A general framework supporting the implementation of targeted maximum likelihood estimators (TMLEs) of a diverse range of statistical target parameters through a unified interface. The goal is that the exposed framework be as general as the mathematical framework upon which it draws.

Maintained by Jeremy Coyle. Last updated 4 months ago.

causal-inference machine-learning targeted-learning variable-importance

5.3 match 38 stars 7.91 score 286 scripts 5 dependents

jmpsteen

medflex:Flexible Mediation Analysis Using Natural Effect Models

Run flexible mediation analyses using natural effect models as described in Lange, Vansteelandt and Bekaert (2012) <DOI:10.1093/aje/kwr525>, Vansteelandt, Bekaert and Lange (2012) <DOI:10.1515/2161-962X.1014> and Loeys, Moerkerke, De Smet, Buysse, Steen and Vansteelandt (2013) <DOI:10.1080/00273171.2013.832132>.

Maintained by Johan Steen. Last updated 2 years ago.

causal-inference flexible-modeling mediation-analysis

5.3 match 23 stars 7.09 score 54 scripts

jackdunnnz

iai:Interface to 'Interpretable AI' Modules

An interface to the algorithms of 'Interpretable AI' <https://www.interpretable.ai> from the R programming language. 'Interpretable AI' provides various modules, including 'Optimal Trees' for classification, regression, prescription and survival analysis, 'Optimal Imputation' for missing data imputation and outlier detection, and 'Optimal Feature Selection' for exact sparse regression. The 'iai' package is an open-source project. The 'Interpretable AI' software modules are proprietary products, but free academic and evaluation licenses are available.

Maintained by Jack Dunn. Last updated 5 months ago.

13.0 match 1 stars 2.00 score 7 scripts

juhakarvanen

R6causal:R6 Class for Structural Causal Models

The implemented R6 class 'SCM' aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages 'igraph' or 'qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages 'causaleffect' and 'dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package 'dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation.

Maintained by Juha Karvanen. Last updated 1 years ago.

9.6 match 1 stars 2.70 score 3 scripts

rvlenth

emmeans:Estimated Marginal Means, aka Least-Squares Means

Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.

Maintained by Russell V. Lenth. Last updated 5 days ago.

1.3 match 377 stars 19.19 score 13k scripts 187 dependents

cran

ubair:Effects of External Conditions on Air Quality

Analyzes the impact of external conditions on air quality using counterfactual approaches, featuring methods for data preparation, modeling, and visualization.

Maintained by Imke Voss. Last updated 2 months ago.

7.1 match 3.18 score

skgrange

rmweather:Tools to Conduct Meteorological Normalisation and Counterfactual Modelling for Air Quality Data

An integrated set of tools to allow data users to conduct meteorological normalisation and counterfactual modelling for air quality data. The meteorological normalisation technique uses predictive random forest models to remove variation of pollutant concentrations so trends and interventions can be explored in a robust way. For examples, see Grange et al. (2018) <doi:10.5194/acp-18-6223-2018> and Grange and Carslaw (2019) <doi:10.1016/j.scitotenv.2018.10.344>. The random forest models can also be used for counterfactual or business as usual (BAU) modelling by using the models to predict, from the model's perspective, the future. For an example, see Grange et al. (2021) <doi:10.5194/acp-2020-1171>.

Maintained by Stuart K. Grange. Last updated 25 days ago.

3.5 match 49 stars 6.24 score 239 scripts

openpharma

beeca:Binary Endpoint Estimation with Covariate Adjustment

Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.

Maintained by Alex Przybylski. Last updated 4 months ago.

3.8 match 6 stars 5.48 score 8 scripts

lzy318

fect:Fixed Effects Counterfactuals

Estimates causal effects with panel data using the counterfactual methods. It is suitable for panel or time-series cross-sectional analysis with binary treatments under (hypothetically) baseline randomization.It allows a treatment to switch on and off and limited carryover effects. It supports linear factor models, a generalization of gsynth and the matrix completion method. Implementation details can be found in Liu, Wang and Xu (2022) <arXiv:2107.00856>.

Maintained by Ziyi Liu. Last updated 2 years ago.

openblas cpp

7.6 match 2.51 score 64 scripts

gabrielrvsc

ArCo:Artificial Counterfactual Package

Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) <DOI:10.2139/ssrn.2823687>.

Maintained by Gabriel F. R. Vasconcelos. Last updated 7 years ago.

5.6 match 5 stars 3.40 score 6 scripts

hknd23

DeepLearningCausal:Causal Inference with Super Learner and Deep Neural Networks

Functions to estimate Conditional Average Treatment Effects (CATE) and Population Average Treatment Effects on the Treated (PATT) from experimental or observational data using the Super Learner (SL) ensemble method and Deep neural networks. The package first provides functions to implement meta-learners such as the Single-learner (S-learner) and Two-learner (T-learner) described in Künzel et al. (2019) <doi:10.1073/pnas.1804597116> for estimating the CATE. The S- and T-learner are each estimated using the SL ensemble method and deep neural networks. It then provides functions to implement the Ottoboni and Poulos (2020) <doi:10.1515/jci-2018-0035> PATT-C estimator to obtain the PATT from experimental data with noncompliance by using the SL ensemble method and deep neural networks.

Maintained by Nguyen K. Huynh. Last updated 2 months ago.

causal-inference deep-neural-networks machine-learning

3.8 match 2 stars 4.76 score 5 scripts

alexanderlange53

svars:Data-Driven Identification of SVAR Models

Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).

Maintained by Alexander Lange. Last updated 2 years ago.

openblas cpp openmp

2.0 match 46 stars 7.22 score 130 scripts

awamaeva

trajmsm:Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories

Implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) <doi:10.1177/09622802231202384>.

Maintained by Awa Diop. Last updated 1 years ago.

g-computation inverse-probability-weights marginal-structural-models tmle trajectory-analysis

3.7 match 5 stars 3.40 score

sachsmc

causaloptim:An Interface to Specify Causal Graphs and Compute Bounds on Causal Effects

When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) <doi:10.1016/B978-1-55860-332-5.50011-0> and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) <doi:10.1080/10618600.2022.2071905>.

Maintained by Michael C Sachs. Last updated 20 days ago.

1.8 match 16 stars 6.92 score 29 scripts

nhejazi

medshift:Causal mediation analysis for stochastic interventions

Estimators of a parameter arising in the decomposition of the population intervention (in)direct effect of stochastic interventions in causal mediation analysis, including efficient one-step, targeted minimum loss (TML), re-weighting (IPW), and substitution estimators. The parameter estimated constitutes a part of each of the population intervention (in)direct effects. These estimators may be used in assessing population intervention (in)direct effects under stochastic treatment regimes, including incremental propensity score interventions and modified treatment policies. The methodology was first discussed by I Díaz and NS Hejazi (2020) <doi:10.1111/rssb.12362>.

Maintained by Nima Hejazi. Last updated 3 years ago.

causal-inference inverse-probability-weights machine-learning mediation-analysis stochastic-interventions targeted-learning treatment-effects

3.3 match 9 stars 3.73 score 12 scripts

moritzpschwarz

getspanel:General-to-Specific Modelling of Panel Data

Uses several types of indicator saturation and automated General-to-Specific (GETS) modelling from the 'gets' package and applies it to panel data. This allows the detection of structural breaks in panel data, operationalising a reverse causal approach of causal inference, see Pretis and Schwarz (2022) <doi:10.2139/ssrn.4022745>.

Maintained by Moritz Schwarz. Last updated 12 months ago.

econometrics gets

2.0 match 10 stars 5.38 score 24 scripts

maartenbijlsma

cfdecomp:Counterfactual Decomposition: MC Integration of the G-Formula

Provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) <doi:10.4054/MPIDR-WP-2019-004> for more information.

Maintained by Maarten Jacob Bijlsma. Last updated 4 years ago.

3.7 match 1 stars 2.70 score 5 scripts

lmiratrix

simITS:Analysis via Simulation of Interrupted Time Series (ITS) Data

Uses simulation to create prediction intervals for post-policy outcomes in interrupted time series (ITS) designs, following Miratrix (2020) <arXiv:2002.05746>. This package provides methods for fitting ITS models with lagged outcomes and variables to account for temporal dependencies. It then conducts inference via simulation, simulating a set of plausible counterfactual post-policy series to compare to the observed post-policy series. This package also provides methods to visualize such data, and also to incorporate seasonality models and smoothing and aggregation/summarization. This work partially funded by Arnold Ventures in collaboration with MDRC.

Maintained by Luke Miratrix. Last updated 2 years ago.

2.3 match 2 stars 4.30 score

helske

walker:Bayesian Generalized Linear Models with Time-Varying Coefficients

Efficient Bayesian generalized linear models with time-varying coefficients as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for efficient sampling. For non-Gaussian models, the package uses the importance sampling type estimators based on approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).

Maintained by Jouni Helske. Last updated 7 months ago.

bayesian generalized-linear-models mcmc stan time-series openblas cpp

1.5 match 44 stars 6.42 score 15 scripts

bsaul

geex:An API for M-Estimation

Provides a general, flexible framework for estimating parameters and empirical sandwich variance estimator from a set of unbiased estimating equations (i.e., M-estimation in the vein of Stefanski & Boos (2002) <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002) are published in the corresponding Journal of Statistical Software paper "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020) <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample variance corrections.

Maintained by Bradley Saul. Last updated 11 months ago.

asymptotics covariance-estimates covariance-estimation estimate-parameters estimating-equations estimation inference m-estimation robust sandwich

1.3 match 8 stars 7.70 score 131 scripts 2 dependents

tlverse

tmle3shift:Targeted Learning of the Causal Effects of Stochastic Interventions

Targeted maximum likelihood estimation (TMLE) of population-level causal effects under stochastic treatment regimes and related nonparametric variable importance analyses. Tools are provided for TML estimation of the counterfactual mean under a stochastic intervention characterized as a modified treatment policy, such as treatment policies that shift the natural value of the exposure. The causal parameter and estimation were described in Díaz and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x> and an improved estimation approach was given by Díaz and van der Laan (2018) <doi:10.1007/978-3-319-65304-4_14>.

Maintained by Nima Hejazi. Last updated 6 months ago.

causal-inference machine-learning marginal-structural-models stochastic-interventions targeted-learning treatment-effects variable-importance

1.8 match 17 stars 5.33 score 42 scripts 1 dependents

robindenz1

contsurvplot:Visualize the Effect of a Continuous Variable on a Time-to-Event Outcome

Graphically display the (causal) effect of a continuous variable on a time-to-event outcome using multiple different types of plots based on g-computation. Those functions include, among others, survival area plots, survival contour plots, survival quantile plots and 3D surface plots. Due to the use of g-computation, all plot allow confounder-adjustment naturally. For details, see Robin Denz, Nina Timmesfeld (2023) <doi:10.1097/EDE.0000000000001630>.

Maintained by Robin Denz. Last updated 1 years ago.

causal-inference continuous g-computation survival-analysis visualization

1.7 match 12 stars 5.53 score 56 scripts

stizet

SelectionBias:Calculates Bounds for the Selection Bias for Binary Treatment and Outcome Variables

Computes bounds and sensitivity parameters as part of sensitivity analysis for selection bias. Different bounds are provided: the SV (Smith and VanderWeele), AF (assumption-free) bound, GAF (generalized AF), and CAF (counterfactual AF) bounds. The calculation of the sensitivity parameters for the SV and GAF bounds assume an additional dependence structure in form of a generalized M-structure. The bounds can be calculated for any structure as long as the necessary assumptions hold. See Smith and VanderWeele (2019) <doi:10.1097/EDE.0000000000001032>, Zetterstrom and Waernbaum (2022) <doi:10.1515/em-2022-0108> and Zetterstrom (2024) <doi:10.1515/em-2023-0033>.

Maintained by Stina Zetterstrom. Last updated 12 months ago.

2.4 match 1 stars 3.70 score 3 scripts

cran

bayesdistreg:Bayesian Distribution Regression

Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.

Maintained by Emmanuel Tsyawo. Last updated 6 years ago.

3.8 match 1.95 score 18 scripts

andyphilips

dynamac:Dynamic Simulation and Testing for Single-Equation ARDL Models

While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).

Maintained by Soren Jordan. Last updated 4 years ago.

ardl stata time-series time-series-analysis

1.3 match 7 stars 5.59 score 37 scripts 1 dependents

ahoundetoungan

CDatanet:Econometrics of Network Data

Simulating and estimating peer effect models and network formation models. The class of peer effect models includes linear-in-means models (Lee, 2004; <doi:10.1111/j.1468-0262.2004.00558.x>), Tobit models (Xu and Lee, 2015; <doi:10.1016/j.jeconom.2015.05.004>), and discrete numerical data models (Houndetoungan, 2024; <doi:10.2139/ssrn.3721250>). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; <doi:10.3982/ECTA12679>) and exponential random graph models (Mele, 2017; <doi:10.3982/ECTA10400>).

Maintained by Aristide Houndetoungan. Last updated 3 months ago.

openblas cpp openmp

1.7 match 1 stars 3.75 score 14 scripts

pik-piam

mredgebuildings:Prepare data to be used by the EDGE-Buildings model

Prepare data to be used by the EDGE-Buildings model.

Maintained by Robin Hasse. Last updated 4 days ago.

1.6 match 3.72 score

aitask

GPP:Gaussian Process Projection

Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. The package writes Stan code, checks it for convergence and adds artificial noise to prevent overfitting and returns a plot of actual values and estimated counterfactual values using r-base plot.

Maintained by David Carlson. Last updated 4 years ago.

2.7 match 2.00 score

ams329

mzipmed:Mediation using MZIP Model

We implement functions allowing for mediation analysis to be performed in cases where the mediator is a count variable with excess zeroes. First a function is provided allowing users to perform analysis for zero-inflated count variables using the marginalized zero-inflated Poisson (MZIP) model (Long et al. 2014 <DOI:10.1002/sim.6293>). Using the counterfactual approach to mediation and MZIP we can obtain natural direct and indirect effects for the overall population. Using delta method processes variance estimation can be performed instantaneously. Alternatively, bootstrap standard errors can be used. We also provide functions for cases with exposure-mediator interactions with four-way decomposition of total effect.

Maintained by Andrew Sims. Last updated 2 years ago.

1.7 match 1 stars 3.00 score 2 scripts

jamesliley

SPARRAfairness:Analysis of Differential Behaviour of SPARRA Score Across Demographic Groups

The SPARRA risk score (Scottish Patients At Risk of admission and Re-Admission) estimates yearly risk of emergency hospital admission using electronic health records on a monthly basis for most of the Scottish population. This package implements a suite of functions used to analyse the behaviour and performance of the score, focusing particularly on differential performance over demographically-defined groups. It includes useful utility functions to plot receiver-operator-characteristic, precision-recall and calibration curves, draw stock human figures, estimate counterfactual quantities without the need to re-compute risk scores, to simulate a semi-realistic dataset.

Maintained by James Liley. Last updated 4 months ago.

1.8 match 2.70 score 4 scripts

arvsjo

ivtools:Instrumental Variables

Contains tools for instrumental variables estimation. Currently, non-parametric bounds, two-stage estimation and G-estimation are implemented. Balke, A. and Pearl, J. (1997) <doi:10.2307/2965583>, Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) <doi:10.1214/11-STS360>.

Maintained by Arvid Sjolander. Last updated 5 years ago.

cpp

1.7 match 3 stars 2.85 score 39 scripts 1 dependents

kdrachal

dynmix:Estimation of Dynamic Finite Mixtures

Allows to perform the dynamic mixture estimation with state-space components and normal regression components, and clustering with normal mixture. Quasi-Bayesian estimation, as well as, that based on the Kerridge inaccuracy approximation are implemented. Main references: Nagy and Suzdaleva (2013) <doi:10.1016/j.apm.2013.05.038>; Nagy et al. (2011) <doi:10.1002/acs.1239>.

Maintained by Krzysztof Drachal. Last updated 2 months ago.

openblas cpp

1.7 match 2.70 score

causalinference

gfoRmula:Parametric G-Formula

Implements the non-iterative conditional expectation (NICE) algorithm of the g-formula algorithm (Robins (1986) <doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)). The g-formula can estimate an outcome's counterfactual mean or risk under hypothetical treatment strategies (interventions) when there is sufficient information on time-varying treatments and confounders. This package can be used for discrete or continuous time-varying treatments and for failure time outcomes or continuous/binary end of follow-up outcomes. The package can handle a random measurement/visit process and a priori knowledge of the data structure, as well as censoring (e.g., by loss to follow-up) and two options for handling competing events for failure time outcomes. Interventions can be flexibly specified, both as interventions on a single treatment or as joint interventions on multiple treatments. See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on how to use the package.

Maintained by Sean McGrath. Last updated 29 days ago.

0.5 match 165 stars 8.18 score 132 scripts

wimvde001

EffectTreat:Prediction of Therapeutic Success

In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.

Maintained by Wim Van der Elst. Last updated 5 years ago.

3.3 match 1.15 score 14 scripts

bbolker

prediction:Tidy, Type-Safe 'prediction()' Methods

A one-function package containing prediction(), a type-safe alternative to predict() that always returns a data frame. The summary() method provides a data frame with average predictions, possibly over counterfactual versions of the data (à la the margins command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' <https://cran.r-project.org/package=margins>. The package currently supports common model types (e.g., lm, glm) from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README file or main package documentation page for a complete listing.

Maintained by Ben Bolker. Last updated 3 months ago.

0.5 match 7.34 score 127 scripts 2 dependents

benkeser

drtmle:Doubly-Robust Nonparametric Estimation and Inference

Targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ van der Laan (2014), <doi:10.1515/ijb-2012-0038>).

Maintained by David Benkeser. Last updated 2 years ago.

causal-inference ensemble-learning iptw statistical-inference tmle

0.5 match 19 stars 6.89 score 90 scripts 1 dependents

cran

IGC.CSM:Simulate Impact of Different Urban Policies Through a General Equilibrium Model

Develops a General Equilibrium (GE) Model, which estimates key variables such as wages, the number of residents and workers, the prices of the floor space, and its distribution between commercial and residential use, as in Ahlfeldt et al., (2015) <doi:10.3982/ECTA10876>. By doing so, the model allows understanding the economic influence of different urban policies.

Maintained by Roman Zarate. Last updated 6 months ago.

2.0 match 1.70 score

davidzarruk

IGCities:Simulate Impact of Different Urban Policies Through a General Equilibrium Model

Develops a General Equilibrium (GE) Model, which estimates key variables such as wages, the number of residents and workers, the prices of the floor space, and its distribution between commercial and residential use, as in Ahlfeldt et al., (2015) <https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA10876>. By doing so, the model allows understanding the economic influence of different urban policies.

Maintained by David Zarruk. Last updated 2 years ago.

2.0 match 1 stars 1.70 score

cran

SetMethods:Functions for Set-Theoretic Multi-Method Research and Advanced QCA

Functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the examples in the books by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press.

Maintained by Ioana-Elena Oana. Last updated 2 years ago.

1.7 match 1 stars 1.80 score 63 scripts

teddy880

RcausalEGM:A General Causal Inference Framework by Encoding Generative Modeling

CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. A description of the methods is given in Liu (2022) <arXiv:2212.05925>.

Maintained by Qiao Liu. Last updated 2 years ago.

1.3 match 2.30 score 6 scripts

thomasjemielita

StratifiedMedicine:Stratified Medicine

A toolkit for stratified medicine, subgroup identification, and precision medicine. Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate models (counterfactual patient-level quantities, such as the conditional average treatment effect), (3) subgroup identification models (find subsets of patients with similar treatment effects), and (4) treatment effect estimation and inference (for the overall population and discovered subgroups). These tools can be customized and are directly used in PRISM (patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019 <arXiv:1912.03337>. This package is in beta and will be continually updated.

Maintained by Thomas Jemielita. Last updated 3 years ago.

0.5 match 2 stars 4.73 score 27 scripts

mavpanos

bunching:Estimate Bunching

Implementation of the bunching estimator for kinks and notches. Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). It produces publication-ready plots in the style followed since Chetty et al. (2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.

Maintained by Panos Mavrokonstantis. Last updated 2 years ago.

0.5 match 5 stars 4.70 score 5 scripts

sachsmc

pseval:Methods for Evaluating Principal Surrogates of Treatment Response

Contains the core methods for the evaluation of principal surrogates in a single clinical trial. Provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation summary methods are provided, including print, summary, plot, and testing.

Maintained by Michael C Sachs. Last updated 6 years ago.

0.5 match 1 stars 3.88 score 15 scripts

cran

gsynth:Generalized Synthetic Control Method

Provides causal inference with interactive fixed-effect models. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. This version supports unbalanced panels and implements the matrix completion method.

Maintained by Yiqing Xu. Last updated 4 years ago.

openblas cpp

0.5 match 2 stars 3.44 score 173 scripts

xinweima

lpdensity:Local Polynomial Density Estimation and Inference

Without imposing stringent distributional assumptions or shape restrictions, nonparametric estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density (and derivatives) estimator based on local polynomial regressions, documented in Cattaneo, Jansson and Ma (2022) <doi:10.18637/jss.v101.i02>: lpdensity() to construct local polynomial based density (and derivatives) estimator, and lpbwdensity() to perform data-driven bandwidth selection.

Maintained by Xinwei Ma. Last updated 5 months ago.

0.5 match 2.50 score 37 scripts 2 dependents

santannaks

LSDirf:Impulse-Response Function Analysis for Agent-Based Models

Performing impulse-response function (IRF) analysis of relevant variables of agent-based simulation models, in particular for models described in 'LSD' format. Based on the data produced by the simulation model, it performs both linear and state-dependent IRF analysis, providing the tools required by the Counterfactual Monte Carlo (CMC) methodology (Amendola and Pereira (2024) <doi:10.2139/ssrn.4740360>), including state identification and sensitivity. CMC proposes retrieving the causal effect of shocks by exploiting the opportunity to directly observe the counterfactual in a fully controlled experimental setup. 'LSD' (Laboratory for Simulation Development) is free software available at <https://www.labsimdev.org/>).

Maintained by Marcelo C. Pereira. Last updated 12 months ago.

0.8 match 2 stars 1.30 score

cran

CICI:Causal Inference with Continuous (Multiple Time Point) Interventions

Estimation of counterfactual outcomes for multiple values of continuous interventions at different time points, and plotting of causal dose-response curves. Details are given in Schomaker, McIlleron, Denti, Diaz (2024) <doi:10.48550/arXiv.2305.06645>.

Maintained by Michael Schomaker. Last updated 3 months ago.

0.5 match 1 stars 1.48 score

cran

MRmediation:A Causal Mediation Method with Methylated Region (MR) as the Mediator

A causal mediation approach under the counterfactual framework to test the significance of total, direct and indirect effects. In this approach, a group of methylated sites from a predefined region are utilized as the mediator, and the functional transformation is used to reduce the possible high dimension in the region-based methylated sites and account for their location information.

Maintained by Qi Yan. Last updated 4 years ago.

0.5 match 1.00 score