Deep Counterfactual Estimation with Categorical Background Variables
This work addresses a key challenge in causal inference for researchers and practitioners, offering a novel solution for counterfactual estimation in scenarios with categorical latent variables.
The paper tackles the problem of estimating counterfactuals from observational data when structural equation models are unavailable, showing that under the assumption of categorical background variables, counterfactuals can be reliably predicted. The proposed CFQP method significantly outperforms existing deep-learning-based approaches on time series and image data.
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.