Matthew Blackwell
Biographic Data
| ID | 3626898 |
|---|---|
| NAME | Matthew Blackwell |
| GIVEN NAMES | Matthew |
| FAMILY NAME | Blackwell |
| SIGNATURE | BLACKWELL M |
| AFFILIATIONS | Harvard University Press |
| ORCID | 0000-0002-3689-9527 |
| VERIFIED | Yes |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 619 |
| AUTHOR COUNT | 15 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2009 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 9 |
Priming Bias Versus Post-Treatment Bias in Experimental Designs
Conditioning on variables affected by treatment can induce post-treatment bias when estimating causal effects. Although this suggests that researchers should measure potential moderators before administering the treatment in an experiment, doing so may also bias causal effect estimation if the covariate measurement primes respondents to react differently to the treatment. This paper formally analyzes this trade-off between post-treatment and prim…
Reducing Model Misspecification and Bias in the Estimation of Interactions
Analyzing variation in treatment effects across subsets of the population is an important way for social scientists to evaluate theoretical arguments. A common strategy in assessing such treatment effect heterogeneity is to include a multiplicative interaction term between the treatment and a hypothesized effect modifier in a regression model. Unfortunately, this approach can result in biased inferences due to unmodeled interactions between the e…
Deep Roots: How Slavery Still Shapes Southern Politics
The lasting effects of slavery on contemporary political attitudes in the American SouthDespite dramatic social transformations in the United States during the last 150 years, the South has remained staunchly conservative. Southerners are more likely to support Republican candidates, gun rights, and the death penalty, and southern whites harbor higher levels of racial resentment than whites in other parts of the country. Why haven't these sentime…
Explaining Preferences from Behavior: A Cognitive Dissonance Approach
The standard approach in positive political theory posits that action choices are the consequences of preferences. Social psychology—in particular, cognitive dissonance theory—suggests the opposite: preferences may themselves be affected by action choices. We present a framework that applies this idea to three models of political choice: (1) one in which partisanship emerges naturally in a two-party system despite policy being multidimensional, (…
Game Changers: Detecting Shifts in Overdispersed Count Data
In this paper, I introduce a Bayesian model for detecting changepoints in a time series of overdispersed counts, such as contributions to candidates over the course of a campaign or counts of terrorist violence. To avoid having to specify the number of changepoint ex ante, this model incorporates a hierarchical Dirichlet process prior to estimate the number of changepoints as well as their location. This allows researchers to discover salient str…
Analyzing Causal Mechanisms in Survey Experiments
Researchers investigating causal mechanisms in survey experiments often rely on nonrandomized quantities to isolate the indirect effect of treatment through these variables. Such an approach, however, requires a “selection-on-observables” assumption, which undermines the advantages of a randomized experiment. In this paper, we show what can be learned about casual mechanisms through experimental design alone. We propose a factorial design that pr…
How to Make Causal Inferences with Time-Series Cross-Sectional Data under Selection on Observables
Repeated measurements of the same countries, people, or groups over time are vital to many fields of political science. These measurements, sometimes called time-series cross-sectional (TSCS) data, allow researchers to estimate a broad set of causal quantities, including contemporaneous effects and direct effects of lagged treatments. Unfortunately, popular methods for TSCS data can only produce valid inferences for lagged effects under some stro…
A Unified Approach to Measurement Error and Missing Data: Details and Extensions
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model specifications and estimation procedures, and analyses to assess the approach's robustness to correlated measur…
A Unified Approach to Measurement Error and Missing Data: Overview and Applications
Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop a…
The Political Legacy of American Slavery
We show that contemporary differences in political attitudes across counties in the American South in part trace their origins to slavery's prevalence more than 150 years ago. Whites who currently live in Southern counties that had high shares of slaves in 1860 are more likely to identify as a Republican, oppose affirmative action, and express racial resentment and colder feelings toward blacks. We show that these results cannot be explained by e…
Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects
Researchers seeking to establish causal relationships frequently control for variables on the purported causal pathway, checking whether the original treatment effect then disappears. Unfortunately, this common approach may lead to biased estimates. In this article, we show that the bias can be avoided by focusing on a quantity of interest called the controlled direct effect. Under certain conditions, the controlled direct effect enables research…
A Selection Bias Approach to Sensitivity Analysis for Causal Effects
The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This article combines two approaches to sensitivity analysis to provide researchers with a too…
A Framework for Dynamic Causal Inference in Political Science
Dynamic strategies are an essential part of politics. In the context of campaigns, for example, candidates continuously recalibrate their campaign strategy in response to polls and opponent actions. Traditional causal inference methods, however, assume that these dynamic decisions are made all at once, an assumption that forces a choice between omitted variable bias and posttreatment bias. Thus, these kinds of “single‐shot” causal inference metho…
Amelia II: A Program for Missing Data
Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives essentially the same answers. The program also improves imputation models by allowing researchers to put Bayesian priors on individual cell values, there…
Cem: Coarsened Exact Matching in Stata
In this article, we introduce a Stata implementation of coarsened exact matching, a new method for improving the estimation of causal effects by reducing imbalance in covariates between treated and control groups. Coarsened exact matching is faster, is easier to use and understand, requires fewer assumptions, is more easily automated, and possesses more attractive statistical properties for many applications than do existing matching methods. In …
Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects
Researchers seeking to establish causal relationships frequently control for variables on the purported causal pathway, checking whether the original treatment effect then disappears. Unfortunately, this common approach may lead to biased estimates. In this article, we show that the bias can be avoided by focusing on a quantity of interest called the controlled direct effect. Under certain conditions, the controlled direct effect enables research…
The Political Legacy of American Slavery
We show that contemporary differences in political attitudes across counties in the American South in part trace their origins to slavery's prevalence more than 150 years ago. Whites who currently live in Southern counties that had high shares of slaves in 1860 are more likely to identify as a Republican, oppose affirmative action, and express racial resentment and colder feelings toward blacks. We show that these results cannot be explained by e…
A Framework for Dynamic Causal Inference in Political Science
Dynamic strategies are an essential part of politics. In the context of campaigns, for example, candidates continuously recalibrate their campaign strategy in response to polls and opponent actions. Traditional causal inference methods, however, assume that these dynamic decisions are made all at once, an assumption that forces a choice between omitted variable bias and posttreatment bias. Thus, these kinds of “single‐shot” causal inference metho…
Analyzing Causal Mechanisms in Survey Experiments
Researchers investigating causal mechanisms in survey experiments often rely on nonrandomized quantities to isolate the indirect effect of treatment through these variables. Such an approach, however, requires a “selection-on-observables” assumption, which undermines the advantages of a randomized experiment. In this paper, we show what can be learned about casual mechanisms through experimental design alone. We propose a factorial design that pr…
Explaining Preferences from Behavior: A Cognitive Dissonance Approach
The standard approach in positive political theory posits that action choices are the consequences of preferences. Social psychology—in particular, cognitive dissonance theory—suggests the opposite: preferences may themselves be affected by action choices. We present a framework that applies this idea to three models of political choice: (1) one in which partisanship emerges naturally in a two-party system despite policy being multidimensional, (…
How to Make Causal Inferences with Time-Series Cross-Sectional Data under Selection on Observables
Repeated measurements of the same countries, people, or groups over time are vital to many fields of political science. These measurements, sometimes called time-series cross-sectional (TSCS) data, allow researchers to estimate a broad set of causal quantities, including contemporaneous effects and direct effects of lagged treatments. Unfortunately, popular methods for TSCS data can only produce valid inferences for lagged effects under some stro…
Reducing Model Misspecification and Bias in the Estimation of Interactions
Analyzing variation in treatment effects across subsets of the population is an important way for social scientists to evaluate theoretical arguments. A common strategy in assessing such treatment effect heterogeneity is to include a multiplicative interaction term between the treatment and a hypothesized effect modifier in a regression model. Unfortunately, this approach can result in biased inferences due to unmodeled interactions between the e…
A Unified Approach to Measurement Error and Missing Data: Overview and Applications
Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop a…
A Selection Bias Approach to Sensitivity Analysis for Causal Effects
The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This article combines two approaches to sensitivity analysis to provide researchers with a too…
A Unified Approach to Measurement Error and Missing Data: Details and Extensions
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model specifications and estimation procedures, and analyses to assess the approach's robustness to correlated measur…
Game Changers: Detecting Shifts in Overdispersed Count Data
In this paper, I introduce a Bayesian model for detecting changepoints in a time series of overdispersed counts, such as contributions to candidates over the course of a campaign or counts of terrorist violence. To avoid having to specify the number of changepoint ex ante, this model incorporates a hierarchical Dirichlet process prior to estimate the number of changepoints as well as their location. This allows researchers to discover salient str…
Priming Bias Versus Post-Treatment Bias in Experimental Designs
Conditioning on variables affected by treatment can induce post-treatment bias when estimating causal effects. Although this suggests that researchers should measure potential moderators before administering the treatment in an experiment, doing so may also bias causal effect estimation if the covariate measurement primes respondents to react differently to the treatment. This paper formally analyzes this trade-off between post-treatment and prim…
Cem: Coarsened Exact Matching in Stata
In this article, we introduce a Stata implementation of coarsened exact matching, a new method for improving the estimation of causal effects by reducing imbalance in covariates between treated and control groups. Coarsened exact matching is faster, is easier to use and understand, requires fewer assumptions, is more easily automated, and possesses more attractive statistical properties for many applications than do existing matching methods. In …
Amelia II: A Program for Missing Data
Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives essentially the same answers. The program also improves imputation models by allowing researchers to put Bayesian priors on individual cell values, there…
A Framework for Dynamic Causal Inference in Political Science
Dynamic strategies are an essential part of politics. In the context of campaigns, for example, candidates continuously recalibrate their campaign strategy in response to polls and opponent actions. Traditional causal inference methods, however, assume that these dynamic decisions are made all at once, an assumption that forces a choice between omitted variable bias and posttreatment bias. Thus, these kinds of “single‐shot” causal inference metho…
A Selection Bias Approach to Sensitivity Analysis for Causal Effects
The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This article combines two approaches to sensitivity analysis to provide researchers with a too…
The Political Legacy of American Slavery
We show that contemporary differences in political attitudes across counties in the American South in part trace their origins to slavery's prevalence more than 150 years ago. Whites who currently live in Southern counties that had high shares of slaves in 1860 are more likely to identify as a Republican, oppose affirmative action, and express racial resentment and colder feelings toward blacks. We show that these results cannot be explained by e…
Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects
Researchers seeking to establish causal relationships frequently control for variables on the purported causal pathway, checking whether the original treatment effect then disappears. Unfortunately, this common approach may lead to biased estimates. In this article, we show that the bias can be avoided by focusing on a quantity of interest called the controlled direct effect. Under certain conditions, the controlled direct effect enables research…
A Unified Approach to Measurement Error and Missing Data: Details and Extensions
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model specifications and estimation procedures, and analyses to assess the approach's robustness to correlated measur…
A Unified Approach to Measurement Error and Missing Data: Overview and Applications
Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop a…
Deep Roots: How Slavery Still Shapes Southern Politics
The lasting effects of slavery on contemporary political attitudes in the American SouthDespite dramatic social transformations in the United States during the last 150 years, the South has remained staunchly conservative. Southerners are more likely to support Republican candidates, gun rights, and the death penalty, and southern whites harbor higher levels of racial resentment than whites in other parts of the country. Why haven't these sentime…
Explaining Preferences from Behavior: A Cognitive Dissonance Approach
The standard approach in positive political theory posits that action choices are the consequences of preferences. Social psychology—in particular, cognitive dissonance theory—suggests the opposite: preferences may themselves be affected by action choices. We present a framework that applies this idea to three models of political choice: (1) one in which partisanship emerges naturally in a two-party system despite policy being multidimensional, (…
Game Changers: Detecting Shifts in Overdispersed Count Data
In this paper, I introduce a Bayesian model for detecting changepoints in a time series of overdispersed counts, such as contributions to candidates over the course of a campaign or counts of terrorist violence. To avoid having to specify the number of changepoint ex ante, this model incorporates a hierarchical Dirichlet process prior to estimate the number of changepoints as well as their location. This allows researchers to discover salient str…
Analyzing Causal Mechanisms in Survey Experiments
Researchers investigating causal mechanisms in survey experiments often rely on nonrandomized quantities to isolate the indirect effect of treatment through these variables. Such an approach, however, requires a “selection-on-observables” assumption, which undermines the advantages of a randomized experiment. In this paper, we show what can be learned about casual mechanisms through experimental design alone. We propose a factorial design that pr…
How to Make Causal Inferences with Time-Series Cross-Sectional Data under Selection on Observables
Repeated measurements of the same countries, people, or groups over time are vital to many fields of political science. These measurements, sometimes called time-series cross-sectional (TSCS) data, allow researchers to estimate a broad set of causal quantities, including contemporaneous effects and direct effects of lagged treatments. Unfortunately, popular methods for TSCS data can only produce valid inferences for lagged effects under some stro…
Reducing Model Misspecification and Bias in the Estimation of Interactions
Analyzing variation in treatment effects across subsets of the population is an important way for social scientists to evaluate theoretical arguments. A common strategy in assessing such treatment effect heterogeneity is to include a multiplicative interaction term between the treatment and a hypothesized effect modifier in a regression model. Unfortunately, this approach can result in biased inferences due to unmodeled interactions between the e…
Priming Bias Versus Post-Treatment Bias in Experimental Designs
Conditioning on variables affected by treatment can induce post-treatment bias when estimating causal effects. Although this suggests that researchers should measure potential moderators before administering the treatment in an experiment, doing so may also bias causal effect estimation if the covariate measurement primes respondents to react differently to the treatment. This paper formally analyzes this trade-off between post-treatment and prim…
Computer Science (11 works) · Econometrics (11 works) · Mathematics (11 works) · Advanced Causal Inference Techniques (10 works) · Statistics (10 works) · Artificial Intelligence (6 works) · Economics (6 works) · Machine learning (6 works) · Psychology (6 works) · Statistical Methods and Inference (6 works)