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causal-inference-missing

This repository contains codes and pipelines associated with the article Doubly robust treatment effect estimation with missing attributes by Mayer et al. (2020).

General use

A full pipeline for estimating treatment effects in the presence of missing attributes, i.e., incomplete confounders and covariates, is provided in pipeline_causal_inference_with_missing_attributes.Rmd. This pipeline can be applied directly on a custom data set (the default is a simulated toy example), provided that it suits the format as follows:

  • X.na: confounders. A data.frame of size #observations x #covariates. With or without missing values.
  • W: treatment assignment. A binary vector coded either with {0,1} or with {FALSE,TRUE} (representing {control,treatment}). Without missing values.
  • Y: observed outcome. A numerical or binary vector (if binary, then coded with {0,1}). Without missing values.

Application: Effect of Tranexamic Acid on Traumatic Brain Injury

The methodology has been applied on a medical question, the effect of the drug tranexamic acid on mortality among traumatic brain injury patients. The data used for this application is extracted from the Traumabase® registry. This registry is only available upon request. However we provide the code used to analyse the data and to estimate the ATE in this context in TranexamicAcid/ate_analysis_traumabase_example.Rmd.

Reference:

Mayer, Imke, Erik Sverdrup, Tobias Gauss, Jean-Denis Moyer, Stefan Wager, and Julie Josse. 2020. "Doubly Robust Treatment Effect Estimation with Missing Attributes." Annals of Applied Statistics 14 (3): 1409–31.

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Code for generating simulations of the causal inference with incomplete confounders paper

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