General Causal Imputation via Synthetic Interventions

TL;DR

This paper introduces a new generalized synthetic intervention estimator for causal imputation, extending prior methods, and demonstrates its effectiveness on synthetic and real datasets.

Contribution

The paper proposes the generalized synthetic interventions (GSI) estimator, expanding causal imputation techniques and proving its identifiability under complex latent models.

Findings

GSI outperforms existing estimators on synthetic data.

GSI effectively recovers unobserved interactions in real datasets.

Theoretical proof of identifiability under complex models.

Abstract

Given two sets of elements (such as cell types and drug compounds), researchers typically only have access to a limited subset of their interactions. The task of causal imputation involves using this subset to predict unobserved interactions. Squires et al. (2022) have proposed two estimators for this task based on the synthetic interventions (SI) estimator: SI-A (for actions) and SI-C (for contexts). We extend their work and introduce a novel causal imputation estimator, generalized synthetic interventions (GSI). We prove the identifiability of this estimator for data generated from a more complex latent factor model. On synthetic and real data we show empirically that it recovers or outperforms their estimators.