1,720,982 research outputs found
Replication Data for: Relaxing Assumptions, Improving Inference: Integrating Machine Learning and the Linear Regression
No full textReplication data and files
Replication Data for: Estimation and Inference on Nonlinear and Heterogeneous Effects
No full textWhile multiple regression offers transparency, interpretability, and desirable theoretical
properties, the method’s simplicity precludes the discovery of complex heterogeneities in the data. We introduce the Method of Direct Estimation and Inference (MDEI) that embraces these potential complexities, is interpretable, has desirable theoretical guarantees, and, unlike some existing methods, returns appropriate uncertainty estimates. The proposed method uses a machine learning regression methodology to estimate the observation-level partial effect, or “slope,” of a treatment variable on an outcome, and allows this value to vary with background covariates. Importantly, we introduce a robust approach to uncertainty estimates. Specifically, we combine a split-sample and conformal strategy to fit a confidence band around the partial effect curve that will contain the true partial effect curve at some controlled proportion of the data, say 90% or 95%, even in the presence of model misspecification. Simulation evidence
and an application illustrate the method’s performance
Replication Data for: Sparse Estimation and Uncertainty with Application to Subgroup Analysis
No full textReplication matrerials for Ratkovic and Tingley (2016) “ Sparse Estimation and Uncertainty with Application to Subgroup Analysis.” All files, data, and scripts needed to generate the figures and results in the paper are in this archive.
The zip file contains two sets of files, for the Bechtel and Scheve (2013) replication and files for replicating the simulation study
Replication Data for: Sparse Estimation and Uncertainty with Application to Subgroup Analysis
No full textReplication matrerials for Ratkovic and Tingley (2016) “ Sparse Estimation and Uncertainty with Application to Subgroup Analysis.” All files, data, and scripts needed to generate the figures and results in the paper are in this archive.
The zip file contains two sets of files, for the Bechtel and Scheve (2013) replication and files for replicating the simulation study
Replication Data for: "Estimating Spatial Preferences from Votes and Text"
No full textThis folder contains the scripts and data necessary to implement Sparse Factor Analysis (SFA) as outline in Kim, Londregan, and Ratkovic (2018). The README file contains all relevant information
Replication Data for: "Estimating Spatial Preferences from Votes and Text"
No full textThis folder contains the scripts and data necessary to implement Sparse Factor Analysis (SFA) as outline in Kim, Londregan, and Ratkovic (2018). The README file contains all relevant information
For “The Intelligence of a Future Day:” Examining the Role of Supreme Court Dissents in the Development of Law
No full textWhen they disagree with the majority’s ruling in a case, Supreme Court justices may write dissenting opinions as an “appeal” to a future Court. I examine what about the topic content of a dissenting opinion could make it a more effective appeal. Theory suggests that in some legal settings, rates of future courts undermining precedents are higher when a dissenting opinion introduces a new topic into the debate, rather than talking about the same issues as the majority but simply disagreeing. I test this theory empirically, drawing on existing work using text methods on Court opinions. I find evidence for the validity of the theory within federalism cases, in which future courts distinguish cases with topically differentiated dissents faster on average. This is consistent with a substantive understanding of federalism cases. However, thinking of dissenters as adding a topic to make undermining precedent less costly for future courts is inconsistent with other empirical results
Evaluation of RAS transition for Salmon Farming; A letter to Mowi’s future largest shareholders.
No full textPRECEDENT, PERFORMANCE, AND PROGRESS: Theorizing and evaluating notions of sexism within oral arguments before the U.S. Supreme Court
No full textProfessional women tend to be more highly scrutinized than their male counterparts, and this has remained the reality in the legal world for much time. Previous research found that female lawyers have historically been discriminated against in their legal careers. For example, women arguing before the Supreme Court were shown to be disproportionately interrupted by the Justices. This study contributes to prior literature by devising a method of conceptualizing gender that is amenable to future changes in court gender dynamics. Through text analysis of the 2010-2015 Supreme Court October Terms, I find insufficient evidence that female lawyers are perform differently than male lawyers and that they are interrupted at significantly different rates than the male lawyers. This, however, is not due to lack statistical power, as I do find evidence for predicting interruptions independent of gender. These results are promising for gender equality within the legal field. Additionally, the results provide a basis for future analysis of oral arguments, which may advance oral arguments to maximize their impact in the Supreme Court
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