2022-06-28-squires22b.md

abstract	booktitle	title	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Consider the problem of determining the effect of a compound on a specific cell type. To answer this question, researchers traditionally need to run an experiment applying the drug of interest to that cell type. This approach is not scalable: given a large number of different actions (compounds) and a large number of different contexts (cell types), it is infeasible to run an experiment for every action-context pair. In such cases, one would ideally like to predict the outcome for every pair while only needing outcome data for a small _subset_ of pairs. This task, which we label "causal imputation", is a generalization of the causal transportability problem. To address this challenge, we extend the recently introduced _synthetic interventions_ (SI) estimator to handle more general data sparsity patterns. We prove that, under a latent factor model, our estimator provides valid estimates for the causal imputation task. We motivate this model by establishing a connection to the linear structural causal model literature. Finally, we consider the prominent CMAP dataset in predicting the effects of compounds on gene expression across cell types. We find that our estimator outperforms standard baselines, thus confirming its utility in biological applications.	First Conference on Causal Learning and Reasoning	Causal Imputation via Synthetic Interventions	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	squires22b	0	Causal Imputation via Synthetic Interventions	688	711	688-711	688	false	Squires, Chandler and Shen, Dennis and Agarwal, Anish and Shah, Devavrat and Uhler, Caroline	given family Chandler Squires given family Dennis Shen given family Anish Agarwal given family Devavrat Shah given family Caroline Uhler	2022-06-28		Proceedings of the First Conference on Causal Learning and Reasoning	177	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v177/squires22b/squires22b.pdf