Naoki Egami
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View article: Beyond reweighting: On the predictive role of covariate shift in effect generalization
Beyond reweighting: On the predictive role of covariate shift in effect generalization Open
Many existing approaches to generalizing statistical inference amid distribution shift operate under the covariate shift assumption, which posits that the conditional distribution of unobserved variables given observable ones is invariant …
View article: Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization
Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization Open
Many existing approaches to generalizing statistical inference amidst distribution shift operate under the covariate shift assumption, which posits that the conditional distribution of unobserved variables given observable ones is invarian…
View article: Identification of causal diffusion effects using placebo outcomes under structural stationarity
Identification of causal diffusion effects using placebo outcomes under structural stationarity Open
Social and biomedical scientists have long been interested in the process through which ideas and behaviours diffuse. In this article, we study an urgent social problem, the spatial diffusion of hate crimes against refugees in Germany, whi…
View article: Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding
Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding Open
Identification and estimation of causal peer effects are challenging in observational studies for two reasons. The first is the identification challenge due to unmeasured network confounding, for example, homophily bias and contextual conf…
View article: Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models Open
In computational social science (CSS), researchers analyze documents to explain social and political phenomena. In most scenarios, CSS researchers first obtain labels for documents and then explain labels using interpretable regression ana…
View article: How to make causal inferences using texts
How to make causal inferences using texts Open
Text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories with large collections of text. Nearly all text-based causal inferences depend on a latent repr…
View article: Elements of External Validity: Framework, Design, and Analysis
Elements of External Validity: Framework, Design, and Analysis Open
The external validity of causal findings is a focus of long-standing debates in the social sciences. Although the issue has been extensively studied at the conceptual level, in practice few empirical studies include an explicit analysis th…
View article: Replication Data for: External Validity: Framework, Design, and Analysis
Replication Data for: External Validity: Framework, Design, and Analysis Open
This file contains replication data and files for "External Validity: Framework, Design, and Analysis". Additionally, it contains the an online supplementary materials with analytical derivations, additional simulation results, supporting …
View article: Replication Data for: How to Make Causal Inferences Using Texts
Replication Data for: How to Make Causal Inferences Using Texts Open
Replication details for How to Make Causal Inferences Using Texts
View article: Using Multiple Pretreatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs
Using Multiple Pretreatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs Open
While a difference-in-differences (DID) design was originally developed with one pre- and one posttreatment period, data from additional pretreatment periods are often available. How can researchers improve the DID design with such multipl…
View article: Replication Data for: Using Multiple Pre-treatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs
Replication Data for: Using Multiple Pre-treatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs Open
While a difference-in-differences (DID) design was originally developed with one pre- and one post-treatment period, data from additional pre-treatment periods are often available. How can researchers improve the DID design with such multi…
View article: Leveraging Population Outcomes to Improve the Generalization of Experimental Results
Leveraging Population Outcomes to Improve the Generalization of Experimental Results Open
Generalizing causal estimates in randomized experiments to a broader target population is essential for guiding decisions by policymakers and practitioners in the social and biomedical sciences. While recent papers developed various weight…
View article: Identification and Estimation of Causal Peer Effects Using Double Negative Controls for Unmeasured Network Confounding
Identification and Estimation of Causal Peer Effects Using Double Negative Controls for Unmeasured Network Confounding Open
Scientists have been interested in estimating causal peer effects to understand how people's behaviors are affected by their network peers. However, it is well known that identification and estimation of causal peer effects are challenging…
View article: Covariate selection for generalizing experimental results: Application to a large-scale development program in Uganda
Covariate selection for generalizing experimental results: Application to a large-scale development program in Uganda Open
Generalizing estimates of causal effects from an experiment to a target population is of interest to scientists. However, researchers are usually constrained by available covariate information. Analysts can often collect many fewer variabl…
View article: Hate Crimes and Gender Imbalances: Fears over Mate Competition and Violence against Refugees
Hate Crimes and Gender Imbalances: Fears over Mate Competition and Violence against Refugees Open
As the number of refugees rises across the world, anti‐refugee violence has become a pressing concern. What explains the incidence and support of such hate crime? We argue that fears among native men that refugees pose a threat in the comp…
View article: Using Multiple Pre-treatment Periods to Improve Difference-in-Differences and Staggered Adoption Design
Using Multiple Pre-treatment Periods to Improve Difference-in-Differences and Staggered Adoption Design Open
While a difference-in-differences (DID) design was originally developed with one pre- and one post-treatment period, data from additional pre-treatment periods are often available. How can researchers improve the DID design with such multi…
View article: Replication Data for: Competing for Loyalists? How Party Positioning Affects Populist Radical Right Voting
Replication Data for: Competing for Loyalists? How Party Positioning Affects Populist Radical Right Voting Open
Replication materials for "Competing for Loyalists? How Party Positioning Affects Populist Radical Right Voting."
View article: Replication Data for: Hate Crimes and Gender Imbalances: Fears over Mate Competition and Violence against Refugees
Replication Data for: Hate Crimes and Gender Imbalances: Fears over Mate Competition and Violence against Refugees Open
As the number of refugees rises across the world, anti-refugee violence has become a pressing concern. What explains the incidence and support of such hate crime? We argue that fears among native men that refugees pose a threat in the comp…
View article: Replication Data for: Spillover Effects in the Presence of Unobserved Networks
Replication Data for: Spillover Effects in the Presence of Unobserved Networks Open
When experimental subjects can interact with each other, the outcome of one individual may be affected by the treatment status of others. In many social science experiments, such spillover effects may occur through multiple networks, for e…