Deep Counterfactual Estimation with Categorical Background Variables

TL;DR

This paper introduces CFQP, a deep learning method for predicting counterfactuals from observational data assuming categorical background variables, outperforming previous methods on time series and image data.

Contribution

The paper proposes a novel counterfactual prediction method that leverages categorical background variables, addressing the challenge of non-identifiability in structural models.

Findings

CFQP outperforms existing deep counterfactual methods

Theoretical guarantees for counterfactual prediction under categorical assumptions

Empirical validation on time series and image datasets

Abstract

Referred to as the third rung of the causal inference ladder, counterfactual queries typically ask the "What if ?" question retrospectively. The standard approach to estimate counterfactuals resides in using a structural equation model that accurately reflects the underlying data generating process. However, such models are seldom available in practice and one usually wishes to infer them from observational data alone. Unfortunately, the correct structural equation model is in general not identifiable from the observed factual distribution. Nevertheless, in this work, we show that under the assumption that the main latent contributors to the treatment responses are categorical, the counterfactuals can be still reliably predicted. Building upon this assumption, we introduce CounterFactual Query Prediction (CFQP), a novel method to infer counterfactuals from continuous observations when the background variables are categorical. We show that our method significantly outperforms previously available deep-learning-based counterfactual methods, both theoretically and empirically on time series and image data. Our code is available at https://github.com/edebrouwer/cfqp.