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Leveraging Causal Graphs for Blocking in Randomized Experiments

Abhishek Kumar Umrawal

arXiv 3 Nov 2021 · Statistics — Methodology

arXiv:2111.02306 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

31
references
35
in-text mentions
31
distinct cited
3
self-citations
8,976
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Judea Pearl (2009) Causality0.92843100%
2RA Fisher (1926) The arrangement of field experiments0.51121100%
3E Smucler, F Sapienza, and A Rotnitzky (2021) Efficient adjustment sets in causal graphical models with hidden variables0.40511100%
4Joseph Berkson (1946) Limitations of the application of fourfold table analysis to hospital data0.40511100%
5Nancy Clements, James J Heckman, and Jeffrey A Smith (1994) Making the most out of social experiments: Reducing the intrinsic uncertainty in evidence from randomized trials with an applica…0.40511100%
6William Gemmell Cochran and Gertrude M Cox (1948) Experimental designs0.40511100%
7Stephen R Cole, Robert W Platt, Enrique F Schisterman, Haitao Chu, D… (2010) Illustrating bias due to conditioning on a collider0.40511100%
8Dan Geiger and Judea Pearl (1990) On the logic of causal models0.40511100%
9Christopher Harshaw, Fredrik Sävje, Daniel Spielman, and Peng Zhang (2019) Balancing covariates in randomized experiments with the gram–schmidt walk design0.40511100%
10James J Heckman (1992) Randomization and social policy evaluation0.40511100%

Showing the top 10 of 31 scored citations.