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Design-based Estimation Theory for Complex Experiments

Haoge Chang

arXiv 12 Nov 2023 · Econometrics · 2 citations (OpenAlex)

arXiv:2311.06891 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper considers the estimation of treatment effects in randomized experiments with complex experimental designs, including cases with interference between units. We develop a design-based estimation theory for arbitrary experimental designs. Our theory facilitates the analysis of many design-estimator pairs that researchers commonly employ in practice and provide procedures to consistently estimate asymptotic variance bounds. We propose new classes of estimators with favorable asymptotic properties from a design-based point of view. In addition, we propose a scalar measure of experimental complexity which can be linked to the design-based variance of the estimators. We demonstrate the performance of our estimators using simulated datasets based on an actual network experiment studying the effect of social networks on insurance adoptions.

Citation extraction

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appendix boundary found by appendix_titled_section at “Proof for Results in Appendix \ref{network_experiments}” · 84% of the source is main text. Read the extracted text to check this.

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
1Aronow, Peter M and Cyrus Samii (2017) Estimating average causal effects under general interference, with application to a social network experiment1.00084100%
2Horn, Roger A and Charles R Johnson (2012) Matrix analysis0.9285480%
3Imbens, Guido W and Donald B Rubin (2015) Causal inference in statistics, social, and biomedical sciences0.9285480%
4Guo, Kevin and Guillaume Basse (2021) The generalized oaxaca-blinder estimator0.92843100%
5Cai, Jing, Alain De Janvry, and Elisabeth Sadoulet (2015) Social networks and the decision to insure0.92314379%
6Middleton, Joel A (2021) b): Unifying Design-based Inference: On bounding and estimating the variance of any linear estimator in any experimental design0.874112100%
7Lin, Winston (2013) Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique0.87492100%
8Cohen, Peter L and Colin B Fogarty (2020) No-harm calibration for generalized oaxaca-blinder estimators0.87452100%
9Middleton, Joel A (2018) A unified theory of regression adjustment for design-based inference0.87452100%
10Freedman, David A (2008) b): On regression adjustments to experimental data0.81142100%

Showing the top 10 of 81 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
1Robust and Efficient Estimation of Potential Outcome Means Under Random Assignment0.40511
2Covariate Adjustment in Stratified Experiments0.40511
3Causal inference in network experiments: regression-based analysis and design-based properties0.40511
4A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances0.40511
5Fixed-Population Causal Inference for Models of Equilibrium0.40511