causalglm

Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning (by tlverse)

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better causalglm alternative or higher similarity.

causalglm reviews and mentions

Posts with mentions or reviews of causalglm. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-12.
  • [Q] Sensitivity of (Causal) Inference to Nonlinear Functional Form
    1 project | /r/statistics | 28 Sep 2021
    Why not both? https://tlverse.org/causalglm/ (Will replace this with a more informative comment when I have free time later today)
  • [Q] Should G-methods, IPTW always be used over traditional regression?
    4 projects | /r/statistics | 12 Sep 2021
    This package: https://github.com/tlverse/causalglm was recently developed to fill the gap between fully black box causal learning methods for heterogeneous treatment effects and fully parametric generalized linear model approaches. It allows for both semiparametric and nonparametric robust causal inference for user defined “working parametric models” for the estimands of interest. It is still black box in that non relevant features of the data distribution are estimated using machine learning but the relevant conditional parameters are modeled fully parametrically (with nonparametric robust inference when misspecified). It is very new so use with caution.

Stats

Basic causalglm repo stats
2
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0.0
about 2 years ago

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