Matias D. Cattaneo, Yihan He, Ruiqi, Yu
arXiv 18 Feb 2025 · Statistics — Methodology
arXiv:2502.13238 · PDF · DOI · OpenAlex · Extracted main text
This paper develops methods for uncertainty quantification in causal inference settings with random network interference. We study the large-sample distributional properties of the classical difference-in-means Hajek treatment effect estimator, and propose a robust inference procedure for the (conditional) direct average treatment effect. Our framework allows for cross-unit interference in both the outcome equation and the treatment assignment mechanism. Drawing from statistical physics, we introduce a novel Ising model to capture complex dependencies in treatment assignment, and derive three results. First, we establish a Berry-Esseen-type distributional approximation that holds pointwise in the degree of interference induced by the Ising model. This approximation recovers existing results in the absence of treatment interference, and highlights the fragility of inference procedures that do not account for the presence of interference in treatment assignment. Second, we establish a uniform distributional approximation for the Hajek estimator and use it to develop robust inference procedures that remain valid uniformly over all interference regimes allowed by the model. Third, we propose a novel resampling method to implement the robust inference procedure and validate its performance through Monte Carlo simulations. A key technical innovation is the introduction of a conditional i.i.d. Gaussianization that may have broader applications. We also discuss extensions and generalizations of our results.
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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 | Norman Bleistein and Richard A Handelsman (1975) Asymptotic Expansions of Integrals | 0.693 | 5 | 1 | 100% |
| 2 | Sourav Chatterjee (2010) Spin glasses and stein’s method | 0.511 | 2 | 1 | 100% |
| 3 | Victor Chernozhukov, Denis Chetverikov, and Kengo Kato (2017) Central limit theorems and bootstrap in high dimensions | 0.511 | 2 | 1 | 100% |
| 4 | Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, and Anthimos… (2021) Learning ising models from one or multiple samples | 0.511 | 2 | 1 | 100% |
| 5 | Peter Eichelsbacher and Matthias Loewe (2010) Stein's method for dependent random variables occuring in statistical mechanics | 0.511 | 2 | 1 | 100% |
| 6 | Bhaswar B Bhattacharya and Sumit Mukherjee (2018) Inference in ising models | 0.405 | 1 | 1 | 100% |
| 7 | Persi Diaconis and David Freedman (1980) de finetti's theorem for markov chains | 0.405 | 1 | 1 | 100% |
| 8 | Persi Diaconis (1988) Recent progress on de finetti’s notions of exchangeability | 0.405 | 1 | 1 | 100% |
| 9 | Richard S Ellis and Charles M Newman (1978) The statistics of curie-weiss models | 0.405 | 1 | 1 | 100% |
| 10 | Sacha Friedli and Yvan Velenik (2017) Statistical Mechanics of Lattice Systems: A Concrete Mathematical Introduction | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 13 scored citations.