Larsvanderlaan/causalCalibration
Code for causal isotonic calibration for heterogeneous treatment effects (appeared in ICML, 2023)
This helps researchers in fields like medicine or social science more accurately estimate the effect of a treatment or intervention across different individuals. You input your observational study data, including information on confounders and treatment assignments, and it outputs refined, more reliable estimates of individualized treatment effects. This is for statisticians, epidemiologists, or data scientists working on causal inference problems.
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Use this if you need to ensure your predicted individual treatment effects from a machine learning model are well-calibrated and accurately reflect the true causal impact.
Not ideal if you are looking for a tool to perform initial causal effect estimation or if your primary interest is in average treatment effects rather than heterogeneous, individual-level effects.
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R
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Aug 18, 2023
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