Explanipedia

Fast computation of exact confidence intervals for randomized experiments with binary outcomes Open

Peter M. Aronow, Haoge Chang, Patrick Lopatto · 2025

On the Foundations of the Design-Based Approach Open

Peter M. Aronow, Austin Jang, Molly Offer‐Westort · 2025

The design-based paradigm may be adopted in causal inference and survey sampling when we assume Rubin's stable unit treatment value assumption (SUTVA) or impose similar frameworks. While often taken for granted, such assumptions entail str…

Rejoinder: Nonparametric identification is not enough, but randomized controlled trials are Open

Peter M. Aronow, James M. Robins, Theo Saarinen, Fredrik Sävje, Jasjeet S. Sekhon · 2025

We thank the editor for organizing a diverse and wide-ranging discussion, and we thank the commentators for their detailed and thoughtful remarks. Most of the commentators provide broader perspectives on randomized experiments and their ro…

Nonparametric identification is not enough, but randomized controlled trials are Open

Peter M. Aronow, James M. Robins, Theo Saarinen, Fredrik Sävje, Jasjeet S. Sekhon · 2025

We argue that randomized controlled trials (RCTs) are special even among studies for which a nonparametric unconfoundedness assumption is credible. This claim follows from two results of Robins and Ritov (1997). First, in settings with at …

Gnostic notes on temporal validity Open

Austin Jang, Molly Offer‐Westort, Serena Wang, Peter M. Aronow · 2024

Kevin Munger argues that, when an agnostic approach is applied to social scientific inquiry, the goal of prediction to new settings is generically impossible. We aim to situate Munger’s critique in a broader scientific and philosophical li…

Randomization-based confidence sets for the local average treatment effect Open

Peter M. Aronow, Haoge Chang, Patrick Lopatto · 2024

We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinement of a randomization-based procedure proposed by Imbens and Rosenbaum (2005) has desirable properties. Namely, we s…

Dyadic Clustering in International Relations Open

Jake Carlson, Trevor Incerti, Peter M. Aronow · 2023

Quantitative empirical inquiry in international relations often relies on dyadic data. Standard analytic techniques do not account for the fact that dyads are not generally independent of one another. That is, when dyads share a constituen…

Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry Open

Serena Wang, Stephen Bates, Peter M. Aronow, Michael I. Jordan · 2023

From the social sciences to machine learning, it has been well documented that metrics to be optimized are not always aligned with social welfare. In healthcare, Dranove et al. (2003) showed that publishing surgery mortality metrics actual…

Fast computation of exact confidence intervals for randomized experiments with binary outcomes Open

Peter M. Aronow, Haoge Chang, Patrick Lopatto · 2023

Given a randomized experiment with binary outcomes, exact confidence intervals for the average causal effect of the treatment can be computed through a series of permutation tests. This approach requires minimal assumptions and is valid fo…

Replication Data for: Dyadic Clustering in International Relations Open

Jake Carlson, Trevor Incerti, Peter M. Aronow · 2023

Quantitative empirical inquiry in international relations often relies on dyadic data. Standard analytic techniques do not account for the fact that dyads are not generally independent of one another. That is, when dyads share a constituen…

On the reliability of published findings using the regression discontinuity design in political science Open

Drew Stommes, Peter M. Aronow, Fredrik Sävje · 2023

The regression discontinuity (RD) design offers identification of causal effects under weak assumptions, earning it a position as a standard method in modern political science research. But identification does not necessarily imply that ca…

Replication Data for: On the reliability of published findings using the regression discontinuity design in political science Open

Drew Stommes, Peter M. Aronow, Fredrik Sävje · 2023

Replication code and data for the analysis in the paper "On the reliability of published findings using the regression discontinuity design in political science" which is accepted for publication in Research & Politics.

Gathering, Evaluating, and Aggregating Social Scientific Models Open

Miriam A. Golden, Tara Slough, Haoyu Zhai, Alexandra Scacco, Macartan Humphreys , et al. · 2023

Negative Consequences of Informing Voters about Deepfakes: Evidence from Two Survey Experiments Open

John Ternovski, Joshua Kalla, Peter M. Aronow · 2022

Advances in machine learning have made possible “deepfakes,” or realistic, computer-generated videos of public figures saying something they have not actually said. Policymakers have expressed concern that deepfakes could mislead voters, b…

A Note on Increases in Inattentive Online Survey-Takers Since 2020 Open

John Ternovski, Lilla Orr, Joshua Kalla, Peter M. Aronow · 2022

Lucid, a popular source of online convenience survey samples, has seen a significant increase in inattentive respondents since 2020. Inattentive participants – respondents who incorrectly answer directed query attention check questions – m…

Exact Bias Correction for Linear Adjustment of Randomized Controlled Trials Open

Haoge Chang, Joel A. Middleton, Peter M. Aronow · 2021

In an influential critique of empirical practice, Freedman (2008) showed that the linear regression estimator was biased for the analysis of randomized controlled trials under the randomization model. Under Freedman's assumptions, we deriv…

On the reliability of published findings using the regression discontinuity design in political science Open

Drew Stommes, Peter M. Aronow, Fredrik Sävje · 2021

The regression discontinuity (RD) design offers identification of causal effects under weak assumptions, earning it a position as a standard method in modern political science research. But identification does not necessarily imply that ca…

Dyadic Clustering in International Relations Open

Jake Carlson, Trevor Incerti, Peter M. Aronow · 2021

Quantitative empirical inquiry in international relations often relies on dyadic data. Standard analytic techniques do not account for the fact that dyads are not generally independent of one another. That is, when dyads share a constituen…

Nonparametric identification is not enough, but randomized controlled trials are Open

Peter M. Aronow, James M. Robins, Theo Saarinen, Fredrik Sävje, Jasjeet S. Sekhon · 2021

We argue that randomized controlled trials (RCTs) are special even among settings where average treatment effects are identified by a nonparametric unconfoundedness assumption. This claim follows from two results of Robins and Ritov (1997)…

Replication Data for: Listwise Deletion In High Dimensions Open

J. Sophia Wang, Peter M. Aronow · 2021

We consider the properties of listwise deletion when both n and the number of variables grow large. We show that when (i) all data has some idiosyncratic missingness and (ii) the number of variables grows superlogarithmically in n, then, f…

Inference in Spatial Experiments with Interference using the SpatialEffect Package Open

Peter M. Aronow, Cyrus Samii, Jonathan A. Sullivan, Ye Wang · 2021

This paper presents methods for analyzing spatial experiments when complex spillovers, displacement effects, and other types of "interference" are present. We present a robust, design-based approach to analyzing effects in such settings. T…

Average treatment effects in the presence of unknown interference Open

Fredrik Sävje, Peter M. Aronow, Michael G. Hudgens · 2021

We investigate large-sample properties of treatment effect estimators under unknown interference in randomized experiments. The inferential target is a generalization of the average treatment effect estimand that marginalizes over potentia…

Listwise Deletion in High Dimensions Open

J. Sophia Wang, Peter M. Aronow · 2021

We consider the properties of listwise deletion when both $n$ and the number of variables grow large. We show that when (i) all data has some idiosyncratic missingness and (ii) the number of variables grows superlogarithmically in $n$, the…

Design-Based Inference for Spatial Experiments under Unknown Interference Open

Peter M. Aronow, Cyrus Samii, Ye Wang · 2020

We consider design-based causal inference for spatial experiments in which treatments may have effects that bleed out and feed back in complex ways. Such spatial spillover effects violate the standard ``no interference'' assumption for sta…

Spillover Effects in Experimental Data Open

Peter M. Aronow, Dean Eckles, Cyrus Samii, Stephanie Zonszein · 2020

We present current methods for estimating treatment effects and spillover effects under "interference", a term which covers a broad class of situations in which a unit's outcome depends not only on treatments received by that unit, but als…

Irregularities in LaCour (2014) Open

David Broockman, Joshua Kalla, Peter M. Aronow · 2020

We report a number of irregularities in the replication dataset posted for LaCour and Green (Science, "When contact changes minds: An experiment on transmission of support for gay equality," 2014) that jointly suggest the dataset (LaCour 2…

Books by Our Readers Open

Brendan Dassey, Michael D. Cicchini, Rowman Littlefield, Frank Biermann, Eva Lövbrand , et al. · 2019

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Replication Data for: A Note on Dropping Experimental Subjects who Fail a Manipulation Check Open

Peter M. Aronow, Jonathon Baron, Lauren E. Pinson · 2018

This repository mirrors the Yale ISPS Data Archive containing the relevant replication files pertaining to Aronow, Baron, Pinson (2018), "A Note on Dropping Experimental Subjects who Fail a Manipulation Check." https://isps.yale.edu/resear…

A Curious Result on Breaking Ties among Sample Medians Open

Peter M. Aronow, Donald Lee · 2018

It is well known that any sample median value (not necessarily unique) minimizes the empirical $L^1$ loss. Interestingly, we show that the minimizer of the $L^{1+\epsilon}$ loss exhibits a singular phenomenon that provides a unique definit…

A note on breaking ties among sample medians Open

Peter M. Aronow, Donald Lee · 2018

Given samples $x_1,\cdots,x_n$, it is well known that any sample median value (not necessarily unique) minimizes the absolute loss $\sum_{i=1}^n |q-x_i|$. Interestingly, we show that the minimizer of the loss $\sum_{i=1}^n|q-x_i|^{1+ε}$ ex…

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