JOSS Papers: causal inference

Published Papers

published Published over 2 years ago

konfound: An R Sensitivity Analysis Package to Quantify the Robustness of Causal Inferences

R

10.21105/joss.05779

published Published over 2 years ago

CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects

R

10.21105/joss.05587

published Published over 2 years ago

SuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in R

R

@blind-contours

10.21105/joss.05422

published Published about 3 years ago

tehtuner: An R package to fit and tune models for the conditional average treatment effect

R

10.21105/joss.05453

published Published over 3 years ago

CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R

R

@blind-contours

10.21105/joss.04181

published Published almost 4 years ago

haldensify: Highly adaptive lasso conditional density estimation in R

R

10.21105/joss.04522

published Published almost 4 years ago

tipr: An R package for sensitivity analyses for unmeasured confounders

R

10.21105/joss.04495

published Published almost 4 years ago

GRAPL: A computational library for nonparametric structural causal modelling, analysis and inference

Python

10.21105/joss.04534

published Published over 4 years ago

medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R

R

10.21105/joss.03979

published Published almost 6 years ago

txshift: Efficient estimation of the causal effects of stochastic interventions in R

R

10.21105/joss.02447

Displaying papers 11 - 20 of 24 in total

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