Peter M Aronow
Biographic Data
| ID | 297010 |
|---|---|
| NAME | Peter M Aronow |
| GIVEN NAMES | Peter M |
| FAMILY NAME | Aronow |
| SIGNATURE | ARONOW P M |
| AFFILIATIONS | Yale University |
| ORCID | 0000-0002-4449-0756 |
| VERIFIED | Yes |
| TOTAL WORKS | 20 |
| TOTAL CITATIONS | 394 |
| AUTHOR COUNT | 20 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 7 |
On the Foundations of the Design-Based Approach
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 strong claims about the data-generating process. We develop an alternative design-based approach: we first invoke a generalized, non-parametric model that allows for unrestricted forms of interference, such as spil…
Gnostic notes on temporal validity
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 literature and to point to ways in which gnosis can and, in some circumstances, must be used to facilitate the accumulation of knowledge. We question some of the premises of Munger’s arguments, such as the definit…
Dyadic Clustering in International Relations
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 constituent member (e.g., a common country), they may be statistically dependent, or “clustered.” Recent work has developed dyadic clustering robust standard errors (DCRSEs) that account for this dependence. Using these D…
On the reliability of published findings using the regression discontinuity design in political science
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 causal effects can be estimated accurately with limited data. In this paper, we highlight that estimation under the RD design involves serious statistical challenges and investigate how these challenges manifest t…
Listwise Deletion in High Dimensions
We consider the properties of listwise deletion when both n and the number of variables grow large. We show that when (i) all data have some idiosyncratic missingness and (ii) the number of variables grows superlogarithmically in n , then, for large n , listwise deletion will drop all rows with probability 1. Using two canonical datasets from the study of comparative politics and international relations, we provide numerical illustration that the…
Books by Our Readers
An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button
A Note on Dropping Experimental Subjects who Fail a Manipulation Check
Dropping subjects based on the results of a manipulation check following treatment assignment is common practice across the social sciences, presumably to restrict estimates to a subpopulation of subjects who understand the experimental prompt. We show that this practice can lead to serious bias and argue for a focus on what is revealed without discarding subjects. Generalizing results developed in Zhang and Rubin (2003) and Lee (2009) to the cas…
Changing climates of conflict
Significance Despite a surge in policy and research attention to conflict and bullying among adolescents, there is little evidence to suggest that current interventions reduce school conflict. Using a large-scale field experiment, we show that it is possible to reduce conflict with a student-driven intervention. By encouraging a small set of students to take a public stance against typical forms of conflict at their school, our intervention reduc…
Ideologically Extreme Candidates in U.S. Presidential Elections, 1948–2012
Scholars routinely cite the landslide defeats of Barry Goldwater and George McGovern as evidence that American electorates punish extremism in presidential politics. Yet systematic evidence for this view is thin. In this article we use postwar election outcomes to assess the electoral effects of extremism. In testing ten models over the seventeen elections, we find scant evidence of extremism penalties that were either substantively large or clos…
Research Note
Bowers, Fredrickson, and Panagopoulos (2013, Reasoning about interference between units: A general framework, Political Analysis 21(1):97–124; henceforth BFP) showed that one could use Fisher's randomization-based hypothesis testing framework to assess counterfactual causal models of treatment propagation and spillover across social networks. This research note improves the statistical inference presented in BFP (2013) by substituting a test stat…
Does Regression Produce Representative Estimates of Causal Effects
With an unrepresentative sample, the estimate of a causal effect may fail to characterize how effects operate in the population of interest. What is less well understood is that conventional estimation practices for observational studies may produce the same problem even with a representative sample. Causal effects estimated via multiple regression differentially weight each unit's contribution. The “effective sample” that regression uses to gene…
Combining List Experiment and Direct Question Estimates of Sensitive Behavior Prevalence
Survey respondents may give untruthful answers to sensitive questions when asked directly. In recent years, researchers have turned to the list experiment (also known as the item count technique) to overcome this difficulty. While list experiments are arguably less prone to bias than direct questioning, list experiments are also more susceptible to sampling variability. We show that researchers need not abandon direct questioning altogether in or…
Cluster–Robust Variance Estimation for Dyadic Data
Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that errors are thus likely correlated across these dyads. We propose a non-parametric, sandwich-type robust variance estimator for linear regression to account for such clustering in dyadic data. We enum…
Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments
Many estimators of the average treatment effect, including the difference-in-means, may be biased when clusters of units are allocated to treatment. This bias remains even when the number of units within each cluster grows asymptotically large. In this paper, we propose simple, unbiased, location-invariant, and covariate-adjusted estimators of the average treatment effect in experiments with random allocation of clusters, along with associated va…
A Note on Close Elections and Regression Analysis of the Party Incumbency Advantage
Much research has recently been devoted to understanding the effects of party incumbency following close elections, typically using a regression discontinuity design. Researchers have demonstrated that close elections in the US House of Representatives may systematically favor certain types of candidates, and that a research design that focuses on close elections may therefore be inappropriate for estimation of the incumbency advantage. We demons…
Field Experimental Designs for the Study of Media Effects
Field experimentation is a promising but seldom used method for studying the effects of media messages on political attitudes and behavior. The practical challenges of conducting media experiments in real-world settings often come down to securing cooperation from research partners, such as political campaigns. To do so, researchers must be prepared to adapt their experimental designs to satisfy the constraints imposed by research partners and th…
Beyond Late
Political scientists frequently use instrumental variables (IV) estimation to estimate the causal effect of an endogenous treatment variable. However, when the treatment effect is heterogeneous, this estimation strategy only recovers the local average treatment effect (LATE). The LATE is an average treatment effect (ATE) for a subset of the population: units that receive treatment if and only if they are induced by an exogenous IV. However, resea…
Field Experiments and the Study of Voter Turnout
Although field experiments have long been used to study voter turnout, only recently has this research method generated widespread scholarly interest. This article reviews the substantive contributions of the field experimental literature on voter turnout. This literature may be divided into two strands, one that focuses on the question of which campaign tactics do or do not increase turnout and another that uses voter mobilization campaigns to t…
A General Method for Detecting Interference Between Units in Randomized Experiments
Interference between units may pose a threat to unbiased causal inference in randomized controlled experiments. Although the assumption of no interference is often necessary for causal inference, few options are available for testing this assumption. This article presents an ex post method for detecting interference between units in randomized experiments. With a test statistic of the analyst's choice, a conditional randomization test allows for …
Does Knowledge of Constitutional Principles Increase Support for Civil Liberties? Results from a Randomized Field Experiment
For decades, scholars have argued that education causes greater support for civil liberties by increasing students’ exposure to political knowledge and constitutional norms, such as due process and freedom of expression. Support for this claim comes exclusively from observational evidence, principally from cross-sectional surveys. This paper presents the first large-scale experimental test of this proposition. More than 1000 students in 59 high s…
A Note on Dropping Experimental Subjects who Fail a Manipulation Check
Dropping subjects based on the results of a manipulation check following treatment assignment is common practice across the social sciences, presumably to restrict estimates to a subpopulation of subjects who understand the experimental prompt. We show that this practice can lead to serious bias and argue for a focus on what is revealed without discarding subjects. Generalizing results developed in Zhang and Rubin (2003) and Lee (2009) to the cas…
Field Experiments and the Study of Voter Turnout
Although field experiments have long been used to study voter turnout, only recently has this research method generated widespread scholarly interest. This article reviews the substantive contributions of the field experimental literature on voter turnout. This literature may be divided into two strands, one that focuses on the question of which campaign tactics do or do not increase turnout and another that uses voter mobilization campaigns to t…
Cluster–Robust Variance Estimation for Dyadic Data
Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that errors are thus likely correlated across these dyads. We propose a non-parametric, sandwich-type robust variance estimator for linear regression to account for such clustering in dyadic data. We enum…
Does Regression Produce Representative Estimates of Causal Effects
With an unrepresentative sample, the estimate of a causal effect may fail to characterize how effects operate in the population of interest. What is less well understood is that conventional estimation practices for observational studies may produce the same problem even with a representative sample. Causal effects estimated via multiple regression differentially weight each unit's contribution. The “effective sample” that regression uses to gene…
Does Knowledge of Constitutional Principles Increase Support for Civil Liberties? Results from a Randomized Field Experiment
For decades, scholars have argued that education causes greater support for civil liberties by increasing students’ exposure to political knowledge and constitutional norms, such as due process and freedom of expression. Support for this claim comes exclusively from observational evidence, principally from cross-sectional surveys. This paper presents the first large-scale experimental test of this proposition. More than 1000 students in 59 high s…
Beyond Late
Political scientists frequently use instrumental variables (IV) estimation to estimate the causal effect of an endogenous treatment variable. However, when the treatment effect is heterogeneous, this estimation strategy only recovers the local average treatment effect (LATE). The LATE is an average treatment effect (ATE) for a subset of the population: units that receive treatment if and only if they are induced by an exogenous IV. However, resea…
Field Experimental Designs for the Study of Media Effects
Field experimentation is a promising but seldom used method for studying the effects of media messages on political attitudes and behavior. The practical challenges of conducting media experiments in real-world settings often come down to securing cooperation from research partners, such as political campaigns. To do so, researchers must be prepared to adapt their experimental designs to satisfy the constraints imposed by research partners and th…
A General Method for Detecting Interference Between Units in Randomized Experiments
Interference between units may pose a threat to unbiased causal inference in randomized controlled experiments. Although the assumption of no interference is often necessary for causal inference, few options are available for testing this assumption. This article presents an ex post method for detecting interference between units in randomized experiments. With a test statistic of the analyst's choice, a conditional randomization test allows for …
On the reliability of published findings using the regression discontinuity design in political science
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 causal effects can be estimated accurately with limited data. In this paper, we highlight that estimation under the RD design involves serious statistical challenges and investigate how these challenges manifest t…
Ideologically Extreme Candidates in U.S. Presidential Elections, 1948–2012
Scholars routinely cite the landslide defeats of Barry Goldwater and George McGovern as evidence that American electorates punish extremism in presidential politics. Yet systematic evidence for this view is thin. In this article we use postwar election outcomes to assess the electoral effects of extremism. In testing ten models over the seventeen elections, we find scant evidence of extremism penalties that were either substantively large or clos…
Dyadic Clustering in International Relations
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 constituent member (e.g., a common country), they may be statistically dependent, or “clustered.” Recent work has developed dyadic clustering robust standard errors (DCRSEs) that account for this dependence. Using these D…
Research Note
Bowers, Fredrickson, and Panagopoulos (2013, Reasoning about interference between units: A general framework, Political Analysis 21(1):97–124; henceforth BFP) showed that one could use Fisher's randomization-based hypothesis testing framework to assess counterfactual causal models of treatment propagation and spillover across social networks. This research note improves the statistical inference presented in BFP (2013) by substituting a test stat…
Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments
Many estimators of the average treatment effect, including the difference-in-means, may be biased when clusters of units are allocated to treatment. This bias remains even when the number of units within each cluster grows asymptotically large. In this paper, we propose simple, unbiased, location-invariant, and covariate-adjusted estimators of the average treatment effect in experiments with random allocation of clusters, along with associated va…
Listwise Deletion in High Dimensions
We consider the properties of listwise deletion when both n and the number of variables grow large. We show that when (i) all data have some idiosyncratic missingness and (ii) the number of variables grows superlogarithmically in n , then, for large n , listwise deletion will drop all rows with probability 1. Using two canonical datasets from the study of comparative politics and international relations, we provide numerical illustration that the…
Does Knowledge of Constitutional Principles Increase Support for Civil Liberties? Results from a Randomized Field Experiment
For decades, scholars have argued that education causes greater support for civil liberties by increasing students’ exposure to political knowledge and constitutional norms, such as due process and freedom of expression. Support for this claim comes exclusively from observational evidence, principally from cross-sectional surveys. This paper presents the first large-scale experimental test of this proposition. More than 1000 students in 59 high s…
Field Experiments and the Study of Voter Turnout
Although field experiments have long been used to study voter turnout, only recently has this research method generated widespread scholarly interest. This article reviews the substantive contributions of the field experimental literature on voter turnout. This literature may be divided into two strands, one that focuses on the question of which campaign tactics do or do not increase turnout and another that uses voter mobilization campaigns to t…
A General Method for Detecting Interference Between Units in Randomized Experiments
Interference between units may pose a threat to unbiased causal inference in randomized controlled experiments. Although the assumption of no interference is often necessary for causal inference, few options are available for testing this assumption. This article presents an ex post method for detecting interference between units in randomized experiments. With a test statistic of the analyst's choice, a conditional randomization test allows for …
Beyond Late
Political scientists frequently use instrumental variables (IV) estimation to estimate the causal effect of an endogenous treatment variable. However, when the treatment effect is heterogeneous, this estimation strategy only recovers the local average treatment effect (LATE). The LATE is an average treatment effect (ATE) for a subset of the population: units that receive treatment if and only if they are induced by an exogenous IV. However, resea…
A Note on Close Elections and Regression Analysis of the Party Incumbency Advantage
Much research has recently been devoted to understanding the effects of party incumbency following close elections, typically using a regression discontinuity design. Researchers have demonstrated that close elections in the US House of Representatives may systematically favor certain types of candidates, and that a research design that focuses on close elections may therefore be inappropriate for estimation of the incumbency advantage. We demons…
Field Experimental Designs for the Study of Media Effects
Field experimentation is a promising but seldom used method for studying the effects of media messages on political attitudes and behavior. The practical challenges of conducting media experiments in real-world settings often come down to securing cooperation from research partners, such as political campaigns. To do so, researchers must be prepared to adapt their experimental designs to satisfy the constraints imposed by research partners and th…
Combining List Experiment and Direct Question Estimates of Sensitive Behavior Prevalence
Survey respondents may give untruthful answers to sensitive questions when asked directly. In recent years, researchers have turned to the list experiment (also known as the item count technique) to overcome this difficulty. While list experiments are arguably less prone to bias than direct questioning, list experiments are also more susceptible to sampling variability. We show that researchers need not abandon direct questioning altogether in or…
Cluster–Robust Variance Estimation for Dyadic Data
Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that errors are thus likely correlated across these dyads. We propose a non-parametric, sandwich-type robust variance estimator for linear regression to account for such clustering in dyadic data. We enum…
Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments
Many estimators of the average treatment effect, including the difference-in-means, may be biased when clusters of units are allocated to treatment. This bias remains even when the number of units within each cluster grows asymptotically large. In this paper, we propose simple, unbiased, location-invariant, and covariate-adjusted estimators of the average treatment effect in experiments with random allocation of clusters, along with associated va…
Changing climates of conflict
Significance Despite a surge in policy and research attention to conflict and bullying among adolescents, there is little evidence to suggest that current interventions reduce school conflict. Using a large-scale field experiment, we show that it is possible to reduce conflict with a student-driven intervention. By encouraging a small set of students to take a public stance against typical forms of conflict at their school, our intervention reduc…
Ideologically Extreme Candidates in U.S. Presidential Elections, 1948–2012
Scholars routinely cite the landslide defeats of Barry Goldwater and George McGovern as evidence that American electorates punish extremism in presidential politics. Yet systematic evidence for this view is thin. In this article we use postwar election outcomes to assess the electoral effects of extremism. In testing ten models over the seventeen elections, we find scant evidence of extremism penalties that were either substantively large or clos…
Research Note
Bowers, Fredrickson, and Panagopoulos (2013, Reasoning about interference between units: A general framework, Political Analysis 21(1):97–124; henceforth BFP) showed that one could use Fisher's randomization-based hypothesis testing framework to assess counterfactual causal models of treatment propagation and spillover across social networks. This research note improves the statistical inference presented in BFP (2013) by substituting a test stat…
Does Regression Produce Representative Estimates of Causal Effects
With an unrepresentative sample, the estimate of a causal effect may fail to characterize how effects operate in the population of interest. What is less well understood is that conventional estimation practices for observational studies may produce the same problem even with a representative sample. Causal effects estimated via multiple regression differentially weight each unit's contribution. The “effective sample” that regression uses to gene…
Books by Our Readers
An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button
A Note on Dropping Experimental Subjects who Fail a Manipulation Check
Dropping subjects based on the results of a manipulation check following treatment assignment is common practice across the social sciences, presumably to restrict estimates to a subpopulation of subjects who understand the experimental prompt. We show that this practice can lead to serious bias and argue for a focus on what is revealed without discarding subjects. Generalizing results developed in Zhang and Rubin (2003) and Lee (2009) to the cas…
Listwise Deletion in High Dimensions
We consider the properties of listwise deletion when both n and the number of variables grow large. We show that when (i) all data have some idiosyncratic missingness and (ii) the number of variables grows superlogarithmically in n , then, for large n , listwise deletion will drop all rows with probability 1. Using two canonical datasets from the study of comparative politics and international relations, we provide numerical illustration that the…
On the reliability of published findings using the regression discontinuity design in political science
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 causal effects can be estimated accurately with limited data. In this paper, we highlight that estimation under the RD design involves serious statistical challenges and investigate how these challenges manifest t…
Gnostic notes on temporal validity
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 literature and to point to ways in which gnosis can and, in some circumstances, must be used to facilitate the accumulation of knowledge. We question some of the premises of Munger’s arguments, such as the definit…
Dyadic Clustering in International Relations
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 constituent member (e.g., a common country), they may be statistically dependent, or “clustered.” Recent work has developed dyadic clustering robust standard errors (DCRSEs) that account for this dependence. Using these D…
On the Foundations of the Design-Based Approach
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 strong claims about the data-generating process. We develop an alternative design-based approach: we first invoke a generalized, non-parametric model that allows for unrestricted forms of interference, such as spil…
Mathematics (14 works) · Computer Science (12 works) · Econometrics (11 works) · Statistics (11 works) · Advanced Causal Inference Techniques (8 works) · Electoral Systems and Political Participation (8 works) · Psychology (8 works) · Causal inference (7 works) · Economics (6 works) · Inference (6 works)