Adam N Glynn
Biographic Data
| ID | 300772 |
|---|---|
| NAME | Adam N Glynn |
| GIVEN NAMES | Adam N |
| FAMILY NAME | Glynn |
| SIGNATURE | GLYNN A N |
| AFFILIATIONS | Emory University |
| ORCID | 0000-0001-9038-9598 |
| VERIFIED | Yes |
| TOTAL WORKS | 17 |
| TOTAL CITATIONS | 429 |
| AUTHOR COUNT | 17 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 9 |
Post-Instrument Bias
When using instrumental variables, researchers often assume that causal effects are only identified conditional on covariates. We show that the role of these covariates is often unclear and that there exists confusion regarding their ability to mitigate violations of the exclusion restriction. We explain when and how existing adjustment strategies may lead to “post‐instrument” bias. We then discuss assumptions that are sufficient to identify vari…
Partisan schadenfreude and candidate cruelty
We establish the prevalence of partisan schadenfreude—that is, taking “joy in the suffering” of partisan others. Analyzing attitudes on health care, taxation, climate change, and the coronavirus pandemic, we find that a sizable portion of the American mass public engages in partisan schadenfreude and that these attitudes are most expressed by those who are ideologically extreme. Additionally, we find that a sizable portion of the American public …
Post-Instrument Bias in Linear Models
Post-instrument covariates are often included as controls in instrumental variable (IV) analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and without measurement error): IV with post-instrument covariates, IV without post-instrument covariates, an…
Are Police Racially Biased in the Decision to Shoot
We present a theoretical model predicting that racially biased policing produces (1) more use of potentially lethal force by firearms against Black civilians than against White civilians and (2) lower fatality rates for Black civilians than White civilians. We empirically evaluate this second prediction with original officer-involved shooting data from 2010 to 2017 for eight local police jurisdictions, finding that Black fatality rates are signif…
Treatment Effect Deviation as an Alternative to Blinder-Oaxaca Decomposition for Studying Social Inequality
The Blinder-Oaxaca decomposition (BOD) is a popular method for studying the contributions of explanatory factors to social inequality. The results have often been given causal interpretations. While recent work and this article both show that some types of BOD are equivalent to a counterfactual-based treatment effect/selection bias decomposition, this equivalence does not hold in general. Given this lack of general equivalence, in this article ba…
Counterevidence of crime-reduction effects from federal grants of military equipment to local police
Do Officer-Involved Shootings Reduce Citizen Contact with Government
Police use of force bears on central matters of political science, including equality of citizen treatment by government. In light of recent high-profile officer-involved shootings (OIS) that resulted in civilian deaths, we assess whether, conditional on a shooting, a civilian's race predicts fatality during police-civilian interactions. We combine Los Angeles data on OIS with a novel research design to estimate the causal effects of fatal shooti…
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…
Front‐Door Difference‐in‐Differences Estimators
We develop front‐door difference‐in‐differences estimators as an extension of front‐door estimators. Under one‐sided noncompliance, an exclusion restriction, and assumptions analogous to parallel trends assumptions, this extension allows identification when the front‐door criterion does not hold. Even if the assumptions are relaxed, we show that the front‐door and front‐door difference‐in‐differences estimators may be combined to form bounds. Fin…
Increasing Inferential Leverage in the Comparative Method: Placebo Tests in Small-n Research
We delineate the underlying homogeneity assumption, procedural variants, and implications of the comparative method and distinguish this from Mill's method of difference. We demonstrate that additional units can provide 'placebo' tests for the comparative method even if the scope of inference is limited to the two units under comparison. Moreover, such tests may be available even when these units are the most similar pair of units on the control …
Using Qualitative Information to Improve Causal Inference
Using the Rosenbaum (2002, 2009) approach to observational studies, we show how qualitative information can be incorporated into quantitative analyses to improve causal inference in three ways. First, by including qualitative information on outcomes within matched sets, we can ameliorate the consequences of the difficulty of measuring those outcomes, sometimes reducing p‐values. Second, additional information across matched sets enables the const…
Identifying Judicial Empathy: Does Having Daughters Cause Judges to Rule for Women's Issues
In this article, we consider whether personal relationships can affect the way that judges decide cases. To do so, we leverage the natural experiment of a child's gender to identify the effect of having daughters on the votes of judges. Using new data on the family lives of U.S. Courts of Appeals judges, we find that, conditional on the number of children a judge has, judges with daughters consistently vote in a more feminist fashion on gender is…
Alleviating Ecological Bias in Poisson Models Using Optimal Subsampling: The Effects of Jim Crow on Black Illiteracy in the Robinson Data
In many situations, data are available at some aggregate level, but one wishes to estimate the individual-level association between a response and an explanatory variable (or variables). Unfortunately, this endeavor is fraught with difficulties because of the ecological level of the data. The only reliable approach for overcoming the inherent identifiability problem associated with the analysis of ecological data is to supplement the ecological d…
What Can We Learn with Statistical Truth Serum
Due to the inherent sensitivity of many survey questions, a number of researchers have adopted an indirect questioning technique known as the list experiment (or the item-count technique) in order to reduce dishonest or evasive responses. However, standard practice with the list experiment requires a large sample size, utilizes only a difference-in-means estimator, and does not provide a measure of the sensitive item for each respondent. This pap…
The Product and Difference Fallacies for Indirect Effects
Political scientists often cite the importance of mechanism‐specific causal knowledge, both for its intrinsic scientific value and as a necessity for informed policy. This article explains why two common inferential heuristics for mechanism‐specific (i.e., indirect) effects can provide misleading answers, such as sign reversals and false null results, even when linear regressions provide unbiased estimates of constituent effects. Additionally, th…
Why Process Matters for Causal Inference
Our goal in this paper is to provide a formal explanation for how within-unit causal process information (i.e., data on posttreatment variables and partial information on posttreatment counterfactuals) can help to inform causal inferences relating to total effects—the overall effect of an explanatory variable on an outcome variable. The basic idea is that, in many applications, researchers may be able to make more plausible causal assumptions con…
An Introduction to the Augmented Inverse Propensity Weighted Estimator
In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a binary regression model for the propensity score, and (2) specify a regression model for the outcome variable. Perhaps the most interesting property of th…
What Can We Learn with Statistical Truth Serum
Due to the inherent sensitivity of many survey questions, a number of researchers have adopted an indirect questioning technique known as the list experiment (or the item-count technique) in order to reduce dishonest or evasive responses. However, standard practice with the list experiment requires a large sample size, utilizes only a difference-in-means estimator, and does not provide a measure of the sensitive item for each respondent. This pap…
Identifying Judicial Empathy: Does Having Daughters Cause Judges to Rule for Women's Issues
In this article, we consider whether personal relationships can affect the way that judges decide cases. To do so, we leverage the natural experiment of a child's gender to identify the effect of having daughters on the votes of judges. Using new data on the family lives of U.S. Courts of Appeals judges, we find that, conditional on the number of children a judge has, judges with daughters consistently vote in a more feminist fashion on gender is…
An Introduction to the Augmented Inverse Propensity Weighted Estimator
In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a binary regression model for the propensity score, and (2) specify a regression model for the outcome variable. Perhaps the most interesting property of th…
Do Officer-Involved Shootings Reduce Citizen Contact with Government
Police use of force bears on central matters of political science, including equality of citizen treatment by government. In light of recent high-profile officer-involved shootings (OIS) that resulted in civilian deaths, we assess whether, conditional on a shooting, a civilian's race predicts fatality during police-civilian interactions. We combine Los Angeles data on OIS with a novel research design to estimate the causal effects of fatal shooti…
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…
The Product and Difference Fallacies for Indirect Effects
Political scientists often cite the importance of mechanism‐specific causal knowledge, both for its intrinsic scientific value and as a necessity for informed policy. This article explains why two common inferential heuristics for mechanism‐specific (i.e., indirect) effects can provide misleading answers, such as sign reversals and false null results, even when linear regressions provide unbiased estimates of constituent effects. Additionally, th…
Counterevidence of crime-reduction effects from federal grants of military equipment to local police
Using Qualitative Information to Improve Causal Inference
Using the Rosenbaum (2002, 2009) approach to observational studies, we show how qualitative information can be incorporated into quantitative analyses to improve causal inference in three ways. First, by including qualitative information on outcomes within matched sets, we can ameliorate the consequences of the difficulty of measuring those outcomes, sometimes reducing p‐values. Second, additional information across matched sets enables the const…
Why Process Matters for Causal Inference
Our goal in this paper is to provide a formal explanation for how within-unit causal process information (i.e., data on posttreatment variables and partial information on posttreatment counterfactuals) can help to inform causal inferences relating to total effects—the overall effect of an explanatory variable on an outcome variable. The basic idea is that, in many applications, researchers may be able to make more plausible causal assumptions con…
Partisan schadenfreude and candidate cruelty
We establish the prevalence of partisan schadenfreude—that is, taking “joy in the suffering” of partisan others. Analyzing attitudes on health care, taxation, climate change, and the coronavirus pandemic, we find that a sizable portion of the American mass public engages in partisan schadenfreude and that these attitudes are most expressed by those who are ideologically extreme. Additionally, we find that a sizable portion of the American public …
Are Police Racially Biased in the Decision to Shoot
We present a theoretical model predicting that racially biased policing produces (1) more use of potentially lethal force by firearms against Black civilians than against White civilians and (2) lower fatality rates for Black civilians than White civilians. We empirically evaluate this second prediction with original officer-involved shooting data from 2010 to 2017 for eight local police jurisdictions, finding that Black fatality rates are signif…
Treatment Effect Deviation as an Alternative to Blinder-Oaxaca Decomposition for Studying Social Inequality
The Blinder-Oaxaca decomposition (BOD) is a popular method for studying the contributions of explanatory factors to social inequality. The results have often been given causal interpretations. While recent work and this article both show that some types of BOD are equivalent to a counterfactual-based treatment effect/selection bias decomposition, this equivalence does not hold in general. Given this lack of general equivalence, in this article ba…
Front‐Door Difference‐in‐Differences Estimators
We develop front‐door difference‐in‐differences estimators as an extension of front‐door estimators. Under one‐sided noncompliance, an exclusion restriction, and assumptions analogous to parallel trends assumptions, this extension allows identification when the front‐door criterion does not hold. Even if the assumptions are relaxed, we show that the front‐door and front‐door difference‐in‐differences estimators may be combined to form bounds. Fin…
Increasing Inferential Leverage in the Comparative Method: Placebo Tests in Small-n Research
We delineate the underlying homogeneity assumption, procedural variants, and implications of the comparative method and distinguish this from Mill's method of difference. We demonstrate that additional units can provide 'placebo' tests for the comparative method even if the scope of inference is limited to the two units under comparison. Moreover, such tests may be available even when these units are the most similar pair of units on the control …
An Introduction to the Augmented Inverse Propensity Weighted Estimator
In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a binary regression model for the propensity score, and (2) specify a regression model for the outcome variable. Perhaps the most interesting property of th…
Why Process Matters for Causal Inference
Our goal in this paper is to provide a formal explanation for how within-unit causal process information (i.e., data on posttreatment variables and partial information on posttreatment counterfactuals) can help to inform causal inferences relating to total effects—the overall effect of an explanatory variable on an outcome variable. The basic idea is that, in many applications, researchers may be able to make more plausible causal assumptions con…
The Product and Difference Fallacies for Indirect Effects
Political scientists often cite the importance of mechanism‐specific causal knowledge, both for its intrinsic scientific value and as a necessity for informed policy. This article explains why two common inferential heuristics for mechanism‐specific (i.e., indirect) effects can provide misleading answers, such as sign reversals and false null results, even when linear regressions provide unbiased estimates of constituent effects. Additionally, th…
What Can We Learn with Statistical Truth Serum
Due to the inherent sensitivity of many survey questions, a number of researchers have adopted an indirect questioning technique known as the list experiment (or the item-count technique) in order to reduce dishonest or evasive responses. However, standard practice with the list experiment requires a large sample size, utilizes only a difference-in-means estimator, and does not provide a measure of the sensitive item for each respondent. This pap…
Alleviating Ecological Bias in Poisson Models Using Optimal Subsampling: The Effects of Jim Crow on Black Illiteracy in the Robinson Data
In many situations, data are available at some aggregate level, but one wishes to estimate the individual-level association between a response and an explanatory variable (or variables). Unfortunately, this endeavor is fraught with difficulties because of the ecological level of the data. The only reliable approach for overcoming the inherent identifiability problem associated with the analysis of ecological data is to supplement the ecological d…
Using Qualitative Information to Improve Causal Inference
Using the Rosenbaum (2002, 2009) approach to observational studies, we show how qualitative information can be incorporated into quantitative analyses to improve causal inference in three ways. First, by including qualitative information on outcomes within matched sets, we can ameliorate the consequences of the difficulty of measuring those outcomes, sometimes reducing p‐values. Second, additional information across matched sets enables the const…
Identifying Judicial Empathy: Does Having Daughters Cause Judges to Rule for Women's Issues
In this article, we consider whether personal relationships can affect the way that judges decide cases. To do so, we leverage the natural experiment of a child's gender to identify the effect of having daughters on the votes of judges. Using new data on the family lives of U.S. Courts of Appeals judges, we find that, conditional on the number of children a judge has, judges with daughters consistently vote in a more feminist fashion on gender is…
Increasing Inferential Leverage in the Comparative Method: Placebo Tests in Small-n Research
We delineate the underlying homogeneity assumption, procedural variants, and implications of the comparative method and distinguish this from Mill's method of difference. We demonstrate that additional units can provide 'placebo' tests for the comparative method even if the scope of inference is limited to the two units under comparison. Moreover, such tests may be available even when these units are the most similar pair of units on the control …
Front‐Door Difference‐in‐Differences Estimators
We develop front‐door difference‐in‐differences estimators as an extension of front‐door estimators. Under one‐sided noncompliance, an exclusion restriction, and assumptions analogous to parallel trends assumptions, this extension allows identification when the front‐door criterion does not hold. Even if the assumptions are relaxed, we show that the front‐door and front‐door difference‐in‐differences estimators may be combined to form bounds. Fin…
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…
Do Officer-Involved Shootings Reduce Citizen Contact with Government
Police use of force bears on central matters of political science, including equality of citizen treatment by government. In light of recent high-profile officer-involved shootings (OIS) that resulted in civilian deaths, we assess whether, conditional on a shooting, a civilian's race predicts fatality during police-civilian interactions. We combine Los Angeles data on OIS with a novel research design to estimate the causal effects of fatal shooti…
Counterevidence of crime-reduction effects from federal grants of military equipment to local police
Treatment Effect Deviation as an Alternative to Blinder-Oaxaca Decomposition for Studying Social Inequality
The Blinder-Oaxaca decomposition (BOD) is a popular method for studying the contributions of explanatory factors to social inequality. The results have often been given causal interpretations. While recent work and this article both show that some types of BOD are equivalent to a counterfactual-based treatment effect/selection bias decomposition, this equivalence does not hold in general. Given this lack of general equivalence, in this article ba…
Are Police Racially Biased in the Decision to Shoot
We present a theoretical model predicting that racially biased policing produces (1) more use of potentially lethal force by firearms against Black civilians than against White civilians and (2) lower fatality rates for Black civilians than White civilians. We empirically evaluate this second prediction with original officer-involved shooting data from 2010 to 2017 for eight local police jurisdictions, finding that Black fatality rates are signif…
Post-Instrument Bias
When using instrumental variables, researchers often assume that causal effects are only identified conditional on covariates. We show that the role of these covariates is often unclear and that there exists confusion regarding their ability to mitigate violations of the exclusion restriction. We explain when and how existing adjustment strategies may lead to “post‐instrument” bias. We then discuss assumptions that are sufficient to identify vari…
Partisan schadenfreude and candidate cruelty
We establish the prevalence of partisan schadenfreude—that is, taking “joy in the suffering” of partisan others. Analyzing attitudes on health care, taxation, climate change, and the coronavirus pandemic, we find that a sizable portion of the American mass public engages in partisan schadenfreude and that these attitudes are most expressed by those who are ideologically extreme. Additionally, we find that a sizable portion of the American public …
Post-Instrument Bias in Linear Models
Post-instrument covariates are often included as controls in instrumental variable (IV) analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and without measurement error): IV with post-instrument covariates, IV without post-instrument covariates, an…
Mathematics (13 works) · Econometrics (12 works) · Statistics (12 works) · Advanced Causal Inference Techniques (10 works) · Computer Science (9 works) · Psychology (8 works) · Political science (7 works) · Causal inference (6 works) · Law (6 works) · Artificial Intelligence (5 works)