tlverse/causalglm
Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
This project helps researchers and data analysts understand the true impact of a treatment or intervention, even when many other factors are at play. You provide data including baseline characteristics, treatment assignments, and an outcome, and it estimates how the treatment affects the outcome for different groups, while explicitly accounting for confounding factors. This tool is for scientists, public health researchers, or marketing analysts who need to assess treatment effects rigorously.
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Use this if you need to determine the causal effect of an intervention, such as a new drug, policy, or marketing campaign, and want to ensure your findings are reliable even if your initial assumptions about the data relationships are not perfectly correct.
Not ideal if you are looking for simple descriptive statistics or if you are comfortable assuming that your data perfectly fits a traditional linear model.
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Language
R
License
GPL-3.0
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Last pushed
Apr 07, 2022
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