Examining the causal effects of exposure to violence on crime among youth involved in the justice system: Experienced, witnessed, and experienced-witnessed violence.

Previous studies on exposure to violence lack a nuanced understanding of the causal effects of different exposure types on offending behaviors. This study, drawing on Pathways to Desistance Study (PDS) data tracking 1354 adjudicated youths aged 14-18 over 7 years, explores the contemporaneous (cross-sectional), acute (after 1 year), enduring (after 3 years), and long-term (after 6 years) causal effects of violence exposure on property and violent offending. The sample, predominantly male (86%), consisted of White (20%), Black (42%), and other (38%) individuals.

Using deep learning and molecular dynamics simulations to unravel the regulation mechanism of peptides as noncompetitive inhibitor of xanthine oxidase.

Xanthine oxidase (XO) is a crucial enzyme in the development of hyperuricemia and gout. This study focuses on LWM and ALPM, two food-derived inhibitors of XO. We used molecular docking to obtain three systems and then conducted 200 ns molecular dynamics simulations for the Apo, LWM, and ALPM systems.

On high-dimensional Poisson models with measurement error: Hypothesis testing for nonlinear nonconvex optimization.

We study estimation and testing in the Poisson regression model with noisy high dimensional covariates, which has wide applications in analyzing noisy big data. Correcting for the estimation bias due to the covariate noise leads to a non-convex target function to minimize. Treating the high dimensional issue further leads us to augment an amenable penalty term to the target function.

Effective Learning During COVID-19: Multilevel Covariates Matching and Propensity Score Matching.

In large-scale observational data with a hierarchical structure, both clusters and interventions often have more than two levels. Popular methods in the binary treatment literature do not naturally extend to the hierarchical multilevel treatment case. For example, most K-12 and universities have moved to an unprecedented hybrid learning module during the COVID-19 pandemic where learning modes include hybrid and fully remote learning, while students were clustered within a class and school region.

A semiparametric method for evaluating causal effects in the presence of error-prone covariates.

The goal of most empirical studies in social sciences and medical research is to determine whether an alteration in an intervention or a treatment will cause a change in the desired outcome response. Unlike randomized designs, establishing the causal relationship based on observational studies is a challenging problem because the ceteris paribus condition is violated. When the covariates of interest are measured with errors, evaluating the causal effects becomes a thorny issue.

An alternative robust estimator of average treatment effect in causal inference.

The problem of estimating the average treatment effects is important when evaluating the effectiveness of medical treatments or social intervention policies. Most of the existing methods for estimating the average treatment effect rely on some parametric assumptions about the propensity score model or the outcome regression model one way or the other. In reality, both models are prone to misspecification, which can have undue influence on the estimated average treatment effect.

Estimation and inference of error-prone covariate effect in the presence of confounding variables.

We introduce a general single index semiparametric measurement error model for the case that the main covariate of interest is measured with error and modeled parametrically, and where there are many other variables also important to the modeling. We propose a semiparametric bias-correction approach to estimate the effect of the covariate of interest. The resultant estimators are shown to be root- consistent, asymptotically normal and locally efficient.