Pedro Carneiro, Sokbae Lee, Daniel Wilhelm
arXiv 11 Mar 2016 · Statistics — Methodology · publishedEconometrics Journal (2019) · 5 citations (OpenAlex)
arXiv:1603.03675 · PDF · DOI · OpenAlex · Extracted main text
In a randomized control trial, the precision of an average treatment effect estimator can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. We propose the use of pre-experimental data such as a census, or a household survey, to inform the choice of both the sample size and the covariates to be collected. Our procedure seeks to minimize the resulting average treatment effect estimator's mean squared error, subject to the researcher's budget constraint. We rely on a modification of an orthogonal greedy algorithm that is conceptually simple and easy to implement in the presence of a large number of potential covariates, and does not require any tuning parameters. In two empirical applications, we show that our procedure can lead to substantial gains of up to 58%, measured either in terms of reductions in data collection costs or in terms of improvements in the precision of the treatment effect estimator.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | McKenzie (2012) Beyond Baseline and Follow-up: The Case for More T in Experiments | 0.811 | 4 | 2 | 100% |
| 2 | McConnell and Vera-Hernández (2015) Going Beyond Simple Sample Size Calculations: A Practitioner's Guide | 0.644 | 3 | 2 | 67% |
| 3 | Bhattacharya and Dupas (2012) Inferring Welfare Maximizing Treatment Assignment under Budget Constraints | 0.644 | 2 | 2 | 100% |
| 4 | Dominitz and Manski (2016) MORE DATA OR BETTER DATA? A Statistical Decision Problem | 0.644 | 2 | 2 | 100% |
| 5 | List, Sadoff, and Wagner (2011) So You Want to Run an Experiment, Now What? Some Simple Rules of Thumb for Optimal Experimental Design | 0.644 | 2 | 2 | 100% |
| 6 | Hahn, Hirano, and Karlan (2011) Adaptive Experimental Design Using the Propensity Score | 0.644 | 2 | 2 | 100% |
| 7 | Tropp (2004) Greed is Good: Algorithmic Results for Sparse Approximation | 0.644 | 2 | 2 | 100% |
| 8 | Tropp and Gilbert (2007) Signal Recovery from Random Measurements via Orthogonal Matching Pursuit | 0.644 | 2 | 2 | 100% |
| 9 | Attanasio et al (2014) Free Access to Child Care, Labor Supply, and Child Development | 0.585 | 3 | 1 | 100% |
| 10 | Barron, Cohen, Dahmen, and DeVore (2008) Approximation and Learning by Greedy Algorithms | 0.529 | 9 | 2 | 22% |
Showing the top 10 of 38 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Empirical Welfare Maximization with Constraints | 0.405 | 1 | 1 |