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Experimental Design For Causal Inference Through An Optimization Lens

Jinglong Zhao

arXiv 18 Aug 2024 · Statistics — Methodology · 1 citations (OpenAlex)

arXiv:2408.09607 · PDF · DOI · OpenAlex · Extracted main text

Abstract

The study of experimental design offers tremendous benefits for answering causal questions across a wide range of applications, including agricultural experiments, clinical trials, industrial experiments, social experiments, and digital experiments. Although valuable in such applications, the costs of experiments often drive experimenters to seek more efficient designs. Recently, experimenters have started to examine such efficiency questions from an optimization perspective, as experimental design problems are fundamentally decision-making problems. This perspective offers a lot of flexibility in leveraging various existing optimization tools to study experimental design problems. This manuscript thus aims to examine the foundations of experimental design problems in the context of causal inference as viewed through an optimization lens.

Citation extraction

300
references
382
in-text mentions
301
distinct cited
0
self-citations
25,605
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
1Wu CF (1981) On the robustness and efficiency of some randomized designs1.00084100%
2Fedorov VV (2013) Theory of optimal experiments0.84333100%
3Lindley DV (1956) On a measure of the information provided by an experiment0.84333100%
4Pukelsheim F (2006) Optimal design of experiments0.84333100%
5Rigollet P, Hütter JC (2019) High dimensional statistics0.84333100%
6Silvey S (2013) Optimal design: an introduction to the theory for parameter estimation0.84333100%
7Titterington D (1975) Optimal design: some geometrical aspects of d-optimality0.84333100%
8Bai Y (2022) Optimality of matched-pair designs in randomized controlled trials0.81142100%
9Ding P (2023) A first course in causal inference0.81142100%
10Bhat N, Farias VF, Moallemi CC, Sinha D (2020) Near-optimal ab testing0.73732100%

Showing the top 10 of 301 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Experiments0.40511
22501.119960.40511