Evan Munro, Xu Kuang, Stefan Wager
arXiv 23 Sep 2021 · Econometrics · publishedAmerican Economic Review (2025) · 14 citations (OpenAlex)
arXiv:2109.11647 · PDF · DOI · OpenAlex · Extracted main text
Policy-relevant treatment effect estimation in a marketplace setting requires taking into account both the direct benefit of the treatment and any spillovers induced by changes to the market equilibrium. The standard way to address these challenges is to evaluate interventions via cluster-randomized experiments, where each cluster corresponds to an isolated market. This approach, however, cannot be used when we only have access to a single market (or a small number of markets). Here, we show how to identify and estimate policy-relevant treatment effects using a unit-level randomized trial run within a single large market. A standard Bernoulli-randomized trial allows consistent estimation of direct effects, and of treatment heterogeneity measures that can be used for welfare-improving targeting. Estimating spillovers - as well as providing confidence intervals for the direct effect - requires estimates of price elasticities, which we provide using an augmented experimental design. Our results rely on all spillovers being mediated via the (observed) prices of a finite number of traded goods, and the market power of any single unit decaying as the market gets large. We illustrate our results using a simulation calibrated to a conditional cash transfer experiment in the Philippines.
appendix boundary found by appendix_command · 43% of the source is main text. Read the extracted text to check this.
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 | Sävje, Aronow \ Hudgens (2021) `Average treatment effects in the presence of unknown interference', The Annals of Statistics 49(2), 673–701 | 1.000 | 8 | 3 | 100% |
| 2 | Hu, Li \ Wager (2022) `Average treatment effects in the presence of interference', Biometrika (forthcoming) | 0.874 | 6 | 4 | 67% |
| 3 | Filmer, Friedman, Kandpal \ Onishi (2023) `Cash Transfers, Food Prices, and Nutrition Impacts on Ineligible Children', The Review of Economics and Statistics 105(2), 327–… | 0.838 | 17 | 3 | 59% |
| 4 | Baird, Bohren, McIntosh \ Özler (2018) `Optimal design of experiments in the presence of interference', Review of Economics and Statistics 100(5), 844–860 | 0.737 | 3 | 2 | 100% |
| 5 | Li \ Wager (2022) `Random graph asymptotics for treatment effect estimation under network interference', The Annals of Statistics 50(4), 2334–2358 | 0.737 | 3 | 2 | 100% |
| 6 | van der Vaart \ Wellner (1996) Weak Convergence and Empirical Processes: With Applications to Statistics, Springer Science & Business Media | 0.648 | 11 | 4 | 27% |
| 7 | Athey \ Imbens (2016) `Recursive partitioning for heterogeneous causal effects', Proceedings of the National Academy of Sciences 113(27), 7353–7360 | 0.644 | 2 | 2 | 100% |
| 8 | Athey, Tibshirani \ Wager (2019) `Generalized random forests', The Annals of Statistics 47(2), 1148–1178 | 0.644 | 2 | 2 | 100% |
| 9 | Carneiro, Heckman \ Vytlacil (2010) `Evaluating marginal policy changes and the average effect of treatment for individuals at the margin', Econometrica 78(1), 377–… | 0.644 | 2 | 2 | 100% |
| 10 | Castillo (2023) Who benefits from surge pricing? | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 67 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.