Leveraging Causal Graphs for Blocking in Randomized Experiments

Abhishek Kumar Umrawal
Proceedings of the Second Conference on Causal Learning and Reasoning, PMLR 213:222-242, 2023.

Abstract

Randomized experiments are often performed to study the causal effects of interest. Blocking is a technique to precisely estimate the causal effects when the experimental material is not homogeneous. It involves stratifying the available experimental material based on the covariates causing non-homogeneity and then randomizing the treatment within those strata (known as blocks). This eliminates the unwanted effect of the covariates on the causal effects of interest. We investigate the problem of finding a stable set of covariates to be used to form blocks, that minimizes the variance of the causal effect estimates. Using the underlying causal graph, we provide an efficient algorithm to obtain such a set for a general semi-Markovian causal model.

Cite this Paper

BibTeX
@InProceedings{pmlr-v213-umrawal23a, title = {Leveraging Causal Graphs for Blocking in Randomized Experiments}, author = {Umrawal, Abhishek Kumar}, booktitle = {Proceedings of the Second Conference on Causal Learning and Reasoning}, pages = {222--242}, year = {2023}, editor = {van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik}, volume = {213}, series = {Proceedings of Machine Learning Research}, month = {11--14 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v213/umrawal23a/umrawal23a.pdf}, url = {https://proceedings.mlr.press/v213/umrawal23a.html}, abstract = {Randomized experiments are often performed to study the causal effects of interest. Blocking is a technique to precisely estimate the causal effects when the experimental material is not homogeneous. It involves stratifying the available experimental material based on the covariates causing non-homogeneity and then randomizing the treatment within those strata (known as blocks). This eliminates the unwanted effect of the covariates on the causal effects of interest. We investigate the problem of finding a stable set of covariates to be used to form blocks, that minimizes the variance of the causal effect estimates. Using the underlying causal graph, we provide an efficient algorithm to obtain such a set for a general semi-Markovian causal model.} }
Endnote
%0 Conference Paper %T Leveraging Causal Graphs for Blocking in Randomized Experiments %A Abhishek Kumar Umrawal %B Proceedings of the Second Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2023 %E Mihaela van der Schaar %E Cheng Zhang %E Dominik Janzing %F pmlr-v213-umrawal23a %I PMLR %P 222--242 %U https://proceedings.mlr.press/v213/umrawal23a.html %V 213 %X Randomized experiments are often performed to study the causal effects of interest. Blocking is a technique to precisely estimate the causal effects when the experimental material is not homogeneous. It involves stratifying the available experimental material based on the covariates causing non-homogeneity and then randomizing the treatment within those strata (known as blocks). This eliminates the unwanted effect of the covariates on the causal effects of interest. We investigate the problem of finding a stable set of covariates to be used to form blocks, that minimizes the variance of the causal effect estimates. Using the underlying causal graph, we provide an efficient algorithm to obtain such a set for a general semi-Markovian causal model.
APA
Umrawal, A.K.. (2023). Leveraging Causal Graphs for Blocking in Randomized Experiments. Proceedings of the Second Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 213:222-242 Available from https://proceedings.mlr.press/v213/umrawal23a.html.

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