Deep Counterfactual Estimation with Categorical Background VariablesReferred 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.
arxiv.org