Within the field of causal inference, we consider the problem of estimating heterogeneous treatment effects from data. We propose and validate a novel approach for learning feature representations to aid the estimation of the conditional average treatment effect or CATE. Our method focuses on an intermediate layer in a neural network trained to predict the outcome from the features. In contrast to previous approaches that encourage the distribution of representations to be treatment-invariant, we leverage a genetic algorithm that optimizes over representations useful for predicting the outcome to select those less useful for predicting the treatment. This allows us to retain information within the features useful for predicting the outcome even if that information may be related to treatment assignment. We validate our method on synthetic examples and illustrate its use on a real life dataset.
This code accompanies our paper1 and is broken into folders:
-
casual_framework/defines a causal dataset structure and causal metalearners that use standard regression methods to perform inference, along with our machinery for feature engineering -
generated_examples/reproduces numerical experiments described in the paper on synthetic data
The code can be run in a conda environment —
conda env create -f environment.yml
conda activate pearl
python3 experiments_synthetic/nie_wager.pyFootnotes
-
M. Burkhart and G. Ruiz, Neuroevolutionary representations for learning heterogeneous treatment effects, Journal of Computational Science 71 (2023) ↩