Bandwidth Selection for Treatment Choice with Binary Outcomes

This study considers the treatment choice problem when outcome variables are binary. We focus on statistical treatment rules that plug in fitted values based on nonparametric kernel regression and show that optimizing two parameters enables the calculation of the maximum regret. Using this result, we propose a novel bandwidth selection method based on the minimax regret criterion. Finally, we perform a numerical analysis to compare the optimal bandwidth choices for the binary and normally distributed outcomes

Manipulation-Robust Regression Discontinuity Designs

In regression discontinuity designs, manipulation threatens identification. A known channel of harmful manipulations is precise control over the observed assignment, but this channel is only an example. This study uncovers the only other channel: sample selection by deciding manipulation precisely based on the given assignment status. For example, in the assignment design of a qualification exam, self-selection by allowing test retakes precisely based on failing the exam is a precise decision. This precise decision harms identification without precisely controlling the final assignment. For instance, retaking the test never ensures passage, but it distorts the qualification assignment because some students that failed then pass. However, students that have already passed, never fail. This novel channel redefines the justification for identification. Furthermore, under a new auxiliary condition, McCrary (2008)'s test is able to confirm identification and the existing worst-case bounds are nested within our new bounds. In a replication study, another sample selection by analysts appears critical in the robustness of their original conclusion.Comment: This work has been circulated as "Harmless and Detectable Manipulations of the Running Variable in Regression Discontinuity Designs: Tests and Bounds.

Hierarchical Regression Discontinuity Design: Pursuing Subgroup Treatment Effects

Regression discontinuity design (RDD) is widely adopted for causal inference under intervention determined by a continuous variable. While one is interested in treatment effect heterogeneity by subgroups in many applications, RDD typically suffers from small subgroup-wise sample sizes, which makes the estimation results highly instable. To solve this issue, we introduce hierarchical RDD (HRDD), a hierarchical Bayes approach for pursuing treatment effect heterogeneity in RDD. A key feature of HRDD is to employ a pseudo-model based on a loss function to estimate subgroup-level parameters of treatment effects under RDD, and assign a hierarchical prior distribution to ``borrow strength" from other subgroups. The posterior computation can be easily done by a simple Gibbs sampling. We demonstrate the proposed HRDD through simulation and real data analysis, and show that HRDD provides much more stable point and interval estimation than separately applying the standard RDD method to each subgroup.Comment: 21 page

Joint diagnostic test of regression discontinuity designs: multiple testing problem

Current diagnostic tests for regression discontinuity (RD) design face a multiple testing problem. We find a massive over-rejection of the identifying restriction among empirical RD studies published in top-five economics journals. Each test achieves a nominal size of 5%; however, the median number of tests per study is 12. Consequently, more than one-third of studies reject at least one of these tests and their diagnostic procedures are invalid for justifying the identifying assumption. We offer a joint testing procedure to resolve the multiple testing problem. Our procedure is based on a new joint asymptotic normality of local linear estimates and local polynomial density estimates. In simulation studies, our joint testing procedures outperform the Bonferroni correction

Protective Actions of 17 β

Steroid hormones synthesized in and secreted from peripheral endocrine glands pass through the blood-brain barrier and play a role in the central nervous system. In addition, the brain possesses an inherent endocrine system and synthesizes steroid hormones known as neurosteroids. Increasing evidence shows that neuroactive steroids protect the central nervous system from various harmful stimuli. Reports show that the neuroprotective actions of steroid hormones attenuate oxidative stress. In this review, we summarize the antioxidative effects of neuroactive steroids, especially 17β-estradiol and progesterone, on neuronal injury in the central nervous system under various pathological conditions, and then describe our recent findings concerning the neuroprotective actions of 17β-estradiol and progesterone on oxidative neuronal injury induced by organometallic compounds, tributyltin, and methylmercury