General Causal Imputation via Synthetic Interventions

• Main Authors: Jiralerspong, Marco, Jiralerspong, Thomas, Shah, Vedant, Sridhar, Dhanya, Gidel, Gauthier
• Format: Journal Article
• Language: English
• Published: 27-10-2024
• Subjects: Computer Science - Learning; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2410.20647

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.