T Yamamoto
Biographic Data
| ID | 9447 |
|---|---|
| NAME | T Yamamoto |
| GIVEN NAMES | T |
| FAMILY NAME | Yamamoto |
| SIGNATURE | YAMAMOTO T |
| AFFILIATIONS | Massachusetts Institute of Technology |
| ORCID | 0000-0002-8079-7675 |
| VERIFIED | Yes |
| TOTAL WORKS | 25 |
| TOTAL CITATIONS | 2096 |
| AUTHOR COUNT | 25 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1958 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 10 |
Dynamic persuasion: Decay and accumulation of partisan media persuasion
Both academic researchers and political pundits have generally accepted two over-time features of persuasion by partisan media: that the persuasive effects of partisan media might be temporary and decay quickly after a single exposure, and that these effects accumulate from multiple exposures. That effects decay may serve to ameliorate concerns about the broad impact of such media on partisan polarization. Yet the assumption that persuasive effec…
Bayesian Sensitivity Analysis for Unmeasured Confounding in Causal Panel Data Models
Despite the recent methodological advancements in causal panel data analysis, concerns remain about unobserved unit-specific time-varying confounders that cannot be addressed by unit or time fixed effects or their interactions. We develop a Bayesian sensitivity analysis (BSA) method to address the concern. Our proposed method is built upon a general framework combining Rubin’s Bayesian framework for model-based causal inference (Rubin [1978], The…
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…
Media Measurement Matters: Estimating the Persuasive Effects of Partisan Media with Survey and Behavioral Data
To what extent do partisan media influence political attitudes and behavior? Although recent methodological advancements have improved scholars' ability to identify the persuasiveness of partisan media, past studies typically rely on self-reported measures of media preferences, which may deviate from real-world news consumption. Integrating individual-level web-browsing data with a survey experiment, we contrast survey-based indicators of stated …
Tracing Causal Paths from Experimental and Observational Data
The study of causal mechanisms abounds in political science, and causal mediation analysis has grown rapidly across different subfields. Yet, conventional methods for analyzing causal mechanisms are difficult to use when the causal effect of interest involves multiple mediators that are potentially causally dependent—a common scenario in political science applications. This article introduces a general framework for tracing causal paths with mult…
Using Conjoint Experiments to Analyze Election Outcomes: The Essential Role of the Average Marginal Component Effect
Political scientists have increasingly deployed conjoint survey experiments to understand multidimensional choices in various settings. In this paper, we show that the average marginal component effect (AMCE) constitutes an aggregation of individual-level preferences that is meaningful both theoretically and empirically. First, extending previous results to allow for arbitrary randomization distributions, we show how the AMCE represents a summary…
Does Conjoint Analysis Mitigate Social Desirability Bias
How can we elicit honest responses in surveys? Conjoint analysis has become a popular tool to address social desirability bias (SDB), or systematic survey misreporting on sensitive topics. However, there has been no direct evidence showing its suitability for this purpose. We propose a novel experimental design to identify conjoint analysis’s ability to mitigate SDB. Specifically, we compare a standard, fully randomized conjoint design against a …
Conjoint Survey Experiments
Conjoint survey experiments have become a popular method for analyzing multidimensional preferences in political science. If properly implemented, conjoint experiments can obtain reliable measures of multidimensional preferences and estimate causal effects of multiple attributes on hypothetical choices or evaluations. This chapter provides an accessible overview of the methodology for designing, implementing, and analyzing conjoint survey experim…
Publication Biases in Replication Studies
One of the strongest findings across the sciences is that publication bias occurs. Of particular note is a “file drawer bias” where statistically significant results are privileged over nonsignificant results. Recognition of this bias, along with increased calls for “open science,” has led to an emphasis on replication studies. Yet, few have explored publication bias and its consequences in replication studies. We offer a model of the publication…
Identifying voter preferences for politicians’ personal attributes: A conjoint experiment in Japan
Although politicians’ personal attributes are an important component of elections and representation, few studies have rigorously investigated which attributes are most relevant in shaping voters’ preferences for politicians, or whether these preferences vary across different electoral system contexts. We investigate these questions with a conjoint survey experiment using the case of Japan’s mixed-member bicameral system. We find that the attribu…
Persuading the Enemy: Estimating the Persuasive Effects of Partisan Media with the Preference-Incorporating Choice and Assignment Design
Does media choice cause polarization, or merely reflect it? We investigate a critical aspect of this puzzle: How partisan media contribute to attitude polarization among different groups of media consumers. We implement a new experimental design, called the Preference-Incorporating Choice and Assignment (PICA) design, that incorporates both free choice and forced exposure. We estimate jointly the degree of polarization caused by selective exposur…
Measuring Voters’ Multidimensional Policy Preferences with Conjoint Analysis: Application to Japan’s 2014 Election
Representative democracy entails the aggregation of multiple policy issues by parties into competing bundles of policies, or “manifestos,” which are then evaluated holistically by voters in elections. This aggregation process obscures themultidimensionalpolicy preferences underlying a voter’ssinglechoice of party or candidate. We address this problem through a conjoint experiment based on the actual party manifestos in Japan’s 2014 House of Repre…
Validating vignette and conjoint survey experiments against real-world behavior
Significance Little evidence exists on whether preferences about hypothetical choices measured in a survey experiment are driven by the same structural determinants of the actual choices made in the real world. This study answers this question using a natural experiment as a behavioral benchmark. Comparing the results from conjoint and vignette experiments on which attributes of hypothetical immigrants generate support for naturalization with the…
Identifying Mechanisms Behind Policy Interventions via Causal Mediation Analysis: Methods for Policy Analysis
Causal analysis in program evaluation has primarily focused on the question about whether or not a program, or package of policies, has an impact on the targeted outcome of interest. However, it is often of scientific and practical importance to also explain why such impacts occur. In this paper, we introduce causal mediation analysis, a statistical framework for analyzing causal mechanisms that has become increasingly popular in social and medic…
Mediation: R Package for Causal Mediation Analysis
In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensi…
Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments
Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show howconjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In co…
Experimental Designs for Identifying Causal Mechanisms
Experimentation is a powerful methodology that enables scientists to establish causal claims empirically. However, one important criticism is that experiments merely provide a black box view of causality and fail to identify causal mechanisms. Specifically, critics argue that, although experiments can identify average causal effects, they cannot explain the process through which such effects come about. If true, this represents a serious limitati…
Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments
Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of the corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are causally independent of one another. In this article, we consider a set of alternative assumptions t…
Understanding the Past: Statistical Analysis of Causal Attribution
Would the third‐wave democracies have been democratized without prior modernization? What proportion of the past militarized disputes between nondemocracies would have been prevented had those dyads been democratic? Although political scientists often ask these questions of causal attribution, existing quantitative methods fail to address them. This article proposes an alternative statistical methodology based on the widely accepted counterfactua…
Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies
Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficultie…
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal paths between the treatment and outcome variables. In this paper we first prove that under a particular version of sequential ignorability assumption, the average causal mediation effect (ACME) is non…
Causal Mediation Analysis Using R
Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis
Political scientists have long been concerned about the validity of survey measurements. Although many have studied classical measurement error in linear regression models where the error is assumed to arise completely at random, in a number of situations the error may be correlated with the outcome. We analyze the impact of differential measurement error on causal estimation. The proposed nonparametric identification analysis avoids arbitrary mo…
A Ground Radar Survey of Medieval Kiln Sites in Suzu City, Western Japan
A ground‐penetrating radar study of medieval kiln sites located near Suzu city, on the Noto peninsula, was conducted prior to excavation. Archaeological site plans estimated from time slices of the closely‐spaced parallel radar profiles are compared with excavations and a proton magnetometer survey. The results indicate that non‐destructive remote sensing with radar can help to determine effectively the presence and general structural features of…
A List of Books and Articles on Journalism Published in Japan
Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments
Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show howconjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In co…
Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies
Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficultie…
Does Conjoint Analysis Mitigate Social Desirability Bias
How can we elicit honest responses in surveys? Conjoint analysis has become a popular tool to address social desirability bias (SDB), or systematic survey misreporting on sensitive topics. However, there has been no direct evidence showing its suitability for this purpose. We propose a novel experimental design to identify conjoint analysis’s ability to mitigate SDB. Specifically, we compare a standard, fully randomized conjoint design against a …
Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments
Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of the corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are causally independent of one another. In this article, we consider a set of alternative assumptions t…
Identifying voter preferences for politicians’ personal attributes: A conjoint experiment in Japan
Although politicians’ personal attributes are an important component of elections and representation, few studies have rigorously investigated which attributes are most relevant in shaping voters’ preferences for politicians, or whether these preferences vary across different electoral system contexts. We investigate these questions with a conjoint survey experiment using the case of Japan’s mixed-member bicameral system. We find that the attribu…
Using Conjoint Experiments to Analyze Election Outcomes: The Essential Role of the Average Marginal Component Effect
Political scientists have increasingly deployed conjoint survey experiments to understand multidimensional choices in various settings. In this paper, we show that the average marginal component effect (AMCE) constitutes an aggregation of individual-level preferences that is meaningful both theoretically and empirically. First, extending previous results to allow for arbitrary randomization distributions, we show how the AMCE represents a summary…
Measuring Voters’ Multidimensional Policy Preferences with Conjoint Analysis: Application to Japan’s 2014 Election
Representative democracy entails the aggregation of multiple policy issues by parties into competing bundles of policies, or “manifestos,” which are then evaluated holistically by voters in elections. This aggregation process obscures themultidimensionalpolicy preferences underlying a voter’ssinglechoice of party or candidate. We address this problem through a conjoint experiment based on the actual party manifestos in Japan’s 2014 House of Repre…
Persuading the Enemy: Estimating the Persuasive Effects of Partisan Media with the Preference-Incorporating Choice and Assignment Design
Does media choice cause polarization, or merely reflect it? We investigate a critical aspect of this puzzle: How partisan media contribute to attitude polarization among different groups of media consumers. We implement a new experimental design, called the Preference-Incorporating Choice and Assignment (PICA) design, that incorporates both free choice and forced exposure. We estimate jointly the degree of polarization caused by selective exposur…
Identifying Mechanisms Behind Policy Interventions via Causal Mediation Analysis: Methods for Policy Analysis
Causal analysis in program evaluation has primarily focused on the question about whether or not a program, or package of policies, has an impact on the targeted outcome of interest. However, it is often of scientific and practical importance to also explain why such impacts occur. In this paper, we introduce causal mediation analysis, a statistical framework for analyzing causal mechanisms that has become increasingly popular in social and medic…
Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis
Political scientists have long been concerned about the validity of survey measurements. Although many have studied classical measurement error in linear regression models where the error is assumed to arise completely at random, in a number of situations the error may be correlated with the outcome. We analyze the impact of differential measurement error on causal estimation. The proposed nonparametric identification analysis avoids arbitrary mo…
Tracing Causal Paths from Experimental and Observational Data
The study of causal mechanisms abounds in political science, and causal mediation analysis has grown rapidly across different subfields. Yet, conventional methods for analyzing causal mechanisms are difficult to use when the causal effect of interest involves multiple mediators that are potentially causally dependent—a common scenario in political science applications. This article introduces a general framework for tracing causal paths with mult…
Understanding the Past: Statistical Analysis of Causal Attribution
Would the third‐wave democracies have been democratized without prior modernization? What proportion of the past militarized disputes between nondemocracies would have been prevented had those dyads been democratic? Although political scientists often ask these questions of causal attribution, existing quantitative methods fail to address them. This article proposes an alternative statistical methodology based on the widely accepted counterfactua…
Media Measurement Matters: Estimating the Persuasive Effects of Partisan Media with Survey and Behavioral Data
To what extent do partisan media influence political attitudes and behavior? Although recent methodological advancements have improved scholars' ability to identify the persuasiveness of partisan media, past studies typically rely on self-reported measures of media preferences, which may deviate from real-world news consumption. Integrating individual-level web-browsing data with a survey experiment, we contrast survey-based indicators of stated …
Publication Biases in Replication Studies
One of the strongest findings across the sciences is that publication bias occurs. Of particular note is a “file drawer bias” where statistically significant results are privileged over nonsignificant results. Recognition of this bias, along with increased calls for “open science,” has led to an emphasis on replication studies. Yet, few have explored publication bias and its consequences in replication studies. We offer a model of the publication…
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…
A Ground Radar Survey of Medieval Kiln Sites in Suzu City, Western Japan
A ground‐penetrating radar study of medieval kiln sites located near Suzu city, on the Noto peninsula, was conducted prior to excavation. Archaeological site plans estimated from time slices of the closely‐spaced parallel radar profiles are compared with excavations and a proton magnetometer survey. The results indicate that non‐destructive remote sensing with radar can help to determine effectively the presence and general structural features of…
A List of Books and Articles on Journalism Published in Japan
A Ground Radar Survey of Medieval Kiln Sites in Suzu City, Western Japan
A ground‐penetrating radar study of medieval kiln sites located near Suzu city, on the Noto peninsula, was conducted prior to excavation. Archaeological site plans estimated from time slices of the closely‐spaced parallel radar profiles are compared with excavations and a proton magnetometer survey. The results indicate that non‐destructive remote sensing with radar can help to determine effectively the presence and general structural features of…
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal paths between the treatment and outcome variables. In this paper we first prove that under a particular version of sequential ignorability assumption, the average causal mediation effect (ACME) is non…
Causal Mediation Analysis Using R
Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis
Political scientists have long been concerned about the validity of survey measurements. Although many have studied classical measurement error in linear regression models where the error is assumed to arise completely at random, in a number of situations the error may be correlated with the outcome. We analyze the impact of differential measurement error on causal estimation. The proposed nonparametric identification analysis avoids arbitrary mo…
Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies
Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficultie…
Understanding the Past: Statistical Analysis of Causal Attribution
Would the third‐wave democracies have been democratized without prior modernization? What proportion of the past militarized disputes between nondemocracies would have been prevented had those dyads been democratic? Although political scientists often ask these questions of causal attribution, existing quantitative methods fail to address them. This article proposes an alternative statistical methodology based on the widely accepted counterfactua…
Experimental Designs for Identifying Causal Mechanisms
Experimentation is a powerful methodology that enables scientists to establish causal claims empirically. However, one important criticism is that experiments merely provide a black box view of causality and fail to identify causal mechanisms. Specifically, critics argue that, although experiments can identify average causal effects, they cannot explain the process through which such effects come about. If true, this represents a serious limitati…
Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments
Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of the corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are causally independent of one another. In this article, we consider a set of alternative assumptions t…
Mediation: R Package for Causal Mediation Analysis
In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensi…
Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments
Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show howconjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In co…
Validating vignette and conjoint survey experiments against real-world behavior
Significance Little evidence exists on whether preferences about hypothetical choices measured in a survey experiment are driven by the same structural determinants of the actual choices made in the real world. This study answers this question using a natural experiment as a behavioral benchmark. Comparing the results from conjoint and vignette experiments on which attributes of hypothetical immigrants generate support for naturalization with the…
Identifying Mechanisms Behind Policy Interventions via Causal Mediation Analysis: Methods for Policy Analysis
Causal analysis in program evaluation has primarily focused on the question about whether or not a program, or package of policies, has an impact on the targeted outcome of interest. However, it is often of scientific and practical importance to also explain why such impacts occur. In this paper, we introduce causal mediation analysis, a statistical framework for analyzing causal mechanisms that has become increasingly popular in social and medic…
Measuring Voters’ Multidimensional Policy Preferences with Conjoint Analysis: Application to Japan’s 2014 Election
Representative democracy entails the aggregation of multiple policy issues by parties into competing bundles of policies, or “manifestos,” which are then evaluated holistically by voters in elections. This aggregation process obscures themultidimensionalpolicy preferences underlying a voter’ssinglechoice of party or candidate. We address this problem through a conjoint experiment based on the actual party manifestos in Japan’s 2014 House of Repre…
Persuading the Enemy: Estimating the Persuasive Effects of Partisan Media with the Preference-Incorporating Choice and Assignment Design
Does media choice cause polarization, or merely reflect it? We investigate a critical aspect of this puzzle: How partisan media contribute to attitude polarization among different groups of media consumers. We implement a new experimental design, called the Preference-Incorporating Choice and Assignment (PICA) design, that incorporates both free choice and forced exposure. We estimate jointly the degree of polarization caused by selective exposur…
Identifying voter preferences for politicians’ personal attributes: A conjoint experiment in Japan
Although politicians’ personal attributes are an important component of elections and representation, few studies have rigorously investigated which attributes are most relevant in shaping voters’ preferences for politicians, or whether these preferences vary across different electoral system contexts. We investigate these questions with a conjoint survey experiment using the case of Japan’s mixed-member bicameral system. We find that the attribu…
Conjoint Survey Experiments
Conjoint survey experiments have become a popular method for analyzing multidimensional preferences in political science. If properly implemented, conjoint experiments can obtain reliable measures of multidimensional preferences and estimate causal effects of multiple attributes on hypothetical choices or evaluations. This chapter provides an accessible overview of the methodology for designing, implementing, and analyzing conjoint survey experim…
Publication Biases in Replication Studies
One of the strongest findings across the sciences is that publication bias occurs. Of particular note is a “file drawer bias” where statistically significant results are privileged over nonsignificant results. Recognition of this bias, along with increased calls for “open science,” has led to an emphasis on replication studies. Yet, few have explored publication bias and its consequences in replication studies. We offer a model of the publication…
Does Conjoint Analysis Mitigate Social Desirability Bias
How can we elicit honest responses in surveys? Conjoint analysis has become a popular tool to address social desirability bias (SDB), or systematic survey misreporting on sensitive topics. However, there has been no direct evidence showing its suitability for this purpose. We propose a novel experimental design to identify conjoint analysis’s ability to mitigate SDB. Specifically, we compare a standard, fully randomized conjoint design against a …
Media Measurement Matters: Estimating the Persuasive Effects of Partisan Media with Survey and Behavioral Data
To what extent do partisan media influence political attitudes and behavior? Although recent methodological advancements have improved scholars' ability to identify the persuasiveness of partisan media, past studies typically rely on self-reported measures of media preferences, which may deviate from real-world news consumption. Integrating individual-level web-browsing data with a survey experiment, we contrast survey-based indicators of stated …
Tracing Causal Paths from Experimental and Observational Data
The study of causal mechanisms abounds in political science, and causal mediation analysis has grown rapidly across different subfields. Yet, conventional methods for analyzing causal mechanisms are difficult to use when the causal effect of interest involves multiple mediators that are potentially causally dependent—a common scenario in political science applications. This article introduces a general framework for tracing causal paths with mult…
Using Conjoint Experiments to Analyze Election Outcomes: The Essential Role of the Average Marginal Component Effect
Political scientists have increasingly deployed conjoint survey experiments to understand multidimensional choices in various settings. In this paper, we show that the average marginal component effect (AMCE) constitutes an aggregation of individual-level preferences that is meaningful both theoretically and empirically. First, extending previous results to allow for arbitrary randomization distributions, we show how the AMCE represents a summary…
Bayesian Sensitivity Analysis for Unmeasured Confounding in Causal Panel Data Models
Despite the recent methodological advancements in causal panel data analysis, concerns remain about unobserved unit-specific time-varying confounders that cannot be addressed by unit or time fixed effects or their interactions. We develop a Bayesian sensitivity analysis (BSA) method to address the concern. Our proposed method is built upon a general framework combining Rubin’s Bayesian framework for model-based causal inference (Rubin [1978], The…
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…
Dynamic persuasion: Decay and accumulation of partisan media persuasion
Both academic researchers and political pundits have generally accepted two over-time features of persuasion by partisan media: that the persuasive effects of partisan media might be temporary and decay quickly after a single exposure, and that these effects accumulate from multiple exposures. That effects decay may serve to ameliorate concerns about the broad impact of such media on partisan polarization. Yet the assumption that persuasive effec…
Computer Science (17 works) · Mathematics (15 works) · Econometrics (14 works) · Statistics (14 works) · Advanced Causal Inference Techniques (13 works) · Psychology (13 works) · Causal inference (12 works) · Causal model (10 works) · Economics (10 works) · Electoral Systems and Political Participation (9 works)