Jens Ludwig, Sendhil Mullainathan, Jann Spiess
arXiv 5 Jul 2017 · Statistics — Machine Learning · 7 citations (OpenAlex)
arXiv:1707.01473 · PDF · DOI · OpenAlex · Extracted main text
In this paper we present tools for applied researchers that re-purpose off-the-shelf methods from the computer-science field of machine learning to create a "discovery engine" for data from randomized controlled trials (RCTs). The applied problem we seek to solve is that economists invest vast resources into carrying out RCTs, including the collection of a rich set of candidate outcome measures. But given concerns about inference in the presence of multiple testing, economists usually wind up exploring just a small subset of the hypotheses that the available data could be used to test. This prevents us from extracting as much information as possible from each RCT, which in turn impairs our ability to develop new theories or strengthen the design of policy interventions. Our proposed solution combines the basic intuition of reverse regression, where the dependent variable of interest now becomes treatment assignment itself, with methods from machine learning that use the data themselves to flexibly identify whether there is any function of the outcomes that predicts (or has signal about) treatment group status. This leads to correctly-sized tests with appropriate $p$-values, which also have the important virtue of being easy to implement in practice. One open challenge that remains with our work is how to meaningfully interpret the signal that these methods find.
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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 | Kling, J. R., Liebman, J. B., and Katz, L. F (2007) Experimental analysis of neighborhood effects | 0.737 | 3 | 2 | 100% |
| 2 | Romano, J. P. and Wolf, M (2005) Stepwise multiple testing as formalized data snooping | 0.737 | 3 | 2 | 100% |
| 3 | Holm, S (1979) A simple sequentially rejective multiple test procedure | 0.737 | 3 | 2 | 100% |
| 4 | Gagnon-Bartsch, J. and Shem-Tov, Y (2019) The classification permutation test: A flexible approach to testing for covariate imbalance | 0.644 | 2 | 2 | 100% |
| 5 | Benjamini, Y. and Hochberg, Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing | 0.644 | 2 | 2 | 100% |
| 6 | Bonferroni, C. E (1936) Teoria statistica delle classi e calcolo delle probabilita | 0.644 | 2 | 2 | 100% |
| 7 | Dunn, O. J (1961) Multiple comparisons among means | 0.644 | 2 | 2 | 100% |
| 8 | Athey, S. and Imbens, G (2016) Recursive partitioning for heterogeneous causal effects | 0.511 | 2 | 1 | 100% |
| 9 | Friedman, J (2004) On multivariate goodness-of-fit and two-sample testing | 0.511 | 2 | 1 | 100% |
| 10 | Chetty, R., Hendren, N., and Katz, L. F (2016) The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 24 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Causal Inference on Outcomes Learned from Text | 0.928 | 4 | 4 |
| 2 | Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach | 0.737 | 3 | 2 |
| 3 | Using Multiple Outcomes to Adjust Standard Errors for Spatial Correlation | 0.405 | 1 | 1 |