arXiv subjects

Zhengfei Yu

Publications and source records attributed to Zhengfei Yu.

10 recordsLinked to original sources

arXiv2025

Robust Semiparametric Inference for Bayesian Additive Regression Trees

We develop a corrected posterior distribution for semiparametric inference on the population mean under missing-at-random (MAR). The procedure combines Bayesian Additive Regression Trees (BART) with Bayesian-bootstrap reweighting. We derive a new Bernstein-von Mises (BvM) theorem and show that even the one-step posterior contains a bias term in the non-Donsker regime. To remove this term, we introduce RoBART, a posterior correction based on pilot estimators of the outcome regression and propensity score. We establish a BvM theorem for the corrected posterior and develop a cross-fitted version based on fold-specific BART posteriors. The average of fold-specific posterior means of RoBART coincides exactly with the corresponding cross-fitted augmented inverse-probability-weighted estimator, equivalently the double machine learning estimator. RoBART therefore provides a corrected posterior distribution for uncertainty quantification around the same point estimator. In simulations and an empirical illustration, RoBART demonstrates competitive finite-sample performance relative to existing methods.

stat.ME↗

arXiv2025

Inference on the Distribution of Individual Treatment Effects in Nonseparable Triangular Models

In this paper, we develop inference methods for the distribution of heterogeneous individual treatment effects (ITEs) in the nonseparable triangular model with a binary endogenous treatment and a binary instrument of Vuong and Xu (2017) and Feng, Vuong, and Xu (2019). We focus on the estimation of the cumulative distribution function (CDF) of the ITE, which can be used to address a wide range of practically important questions such as inference on the proportion of individuals with positive ITEs, the quantiles of the distribution of ITEs, and the interquartile range as a measure of the spread of the ITEs, as well as comparison of the ITE distributions across sub-populations. Moreover, our CDF-based approach can deliver more precise results than density-based approach previously considered in the literature. We establish weak convergence to tight Gaussian processes for the empirical CDF and quantile function computed from nonparametric ITE estimates of Feng, Vuong, and Xu (2019). Using those results, we develop bootstrap-based nonparametric inferential methods, including uniform confidence bands for the CDF and quantile function of the ITE distribution.

econ.EM↗

arXiv2024

Semiparametric Bayesian Difference-in-Differences

This paper studies semiparametric Bayesian inference for the average treatment effect on the treated (ATT) within the difference-in-differences (DiD) research design. We propose two new Bayesian methods with frequentist validity. The first one is the semiparametric Bayesian outcome regression, where we place a Gaussian process prior on the conditional mean function of the control group. The second method is a doubly robust Bayesian procedure that adjusts the prior distribution of the conditional mean function and subsequently corrects the posterior distribution of the resulting ATT. We prove new semiparametric Bernstein-von Mises (BvM) theorems for both proposals. Monte Carlo simulations and an empirical application demonstrate that the proposed Bayesian DiD methods exhibit strong finite-sample performance. We also present extensions of the canonical DiD approach, incorporating clustered data and staggered entry with multiple periods.

econ.EM↗

arXiv2024

Quasi-Bayesian Estimation and Inference with Control Functions

This paper introduces a quasi-Bayesian method that integrates frequentist nonparametric estimation with Bayesian inference in a two-stage process. Applied to an endogenous discrete choice model, the approach first uses kernel or sieve estimators to estimate the control function nonparametrically, followed by Bayesian methods to estimate the structural parameters. This combination leverages the advantages of both frequentist tractability for nonparametric estimation and Bayesian computational efficiency for complicated structural models. We analyze the asymptotic properties of the resulting quasi-posterior distribution, finding that its mean provides a consistent estimator for the parameters of interest, although its quantiles do not yield valid confidence intervals. However, bootstrapping the quasi-posterior mean accounts for the estimation uncertainty from the first stage, thereby producing asymptotically valid confidence intervals.

econ.EM↗

arXiv2022

Double Robust Bayesian Inference on Average Treatment Effects

We propose a double robust Bayesian inference procedure on the average treatment effect (ATE) under unconfoundedness. For our new Bayesian approach, we first adjust the prior distributions of the conditional mean functions, and then correct the posterior distribution of the resulting ATE. Both adjustments make use of pilot estimators motivated by the semiparametric influence function for ATE estimation. We prove asymptotic equivalence of our Bayesian procedure and efficient frequentist ATE estimators by establishing a new semiparametric Bernstein-von Mises theorem under double robustness; i.e., the lack of smoothness of conditional mean functions can be compensated by high regularity of the propensity score and vice versa. Consequently, the resulting Bayesian credible sets form confidence intervals with asymptotically exact coverage probability. In simulations, our method provides precise point estimates of the ATE through the posterior mean and credible intervals that closely align with the nominal coverage probability. Furthermore, our approach achieves a shorter interval length in comparison to existing methods. We illustrate our method in an application to the National Supported Work Demonstration following LaLonde [1986] and Dehejia and Wahba [1999].

econ.EM↗

arXiv2022

A review of knowledge graph application scenarios in cyber security

Facing the dynamic complex cyber environments, internal and external cyber threat intelligence, and the increasing risk of cyber-attack, knowledge graphs show great application potential in the cyber security area because of their capabilities in knowledge aggregation, representation, management, and reasoning. However, while most research has focused on how to develop a complete knowledge graph, it remains unclear how to apply the knowledge graph to solve industrial real challenges in cyber-attack and defense scenarios. In this review, we provide a brief overview of the basic concepts, schema, and construction approaches for the cyber security knowledge graph. To facilitate future research on cyber security knowledge graphs, we also present a curated collection of datasets and open-source libraries on the knowledge construction and information extraction task. In the major part of this article, we conduct a comparative review of the different works that elaborate on the recent progress in the application scenarios of the cyber security knowledge graph. Furthermore, a novel comprehensive classification framework is created to describe the connected works from nine primary categories and eighteen subcategories. Finally, we have a thorough outlook on several promising research directions based on the discussion of existing research flaws.

cs.CR↗

arXiv2022

Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness

It is well-known that deep neural networks (DNNs) have shown remarkable success in many fields. However, when adding an imperceptible magnitude perturbation on the model input, the model performance might get rapid decrease. To address this issue, a randomness technique has been proposed recently, named Stochastic Neural Networks (SNNs). Specifically, SNNs inject randomness into the model to defend against unseen attacks and improve the adversarial robustness. However, existed studies on SNNs mainly focus on injecting fixed or learnable noises to model weights/activations. In this paper, we find that the existed SNNs performances are largely bottlenecked by the feature representation ability. Surprisingly, simply maximizing the variance per dimension of the feature distribution leads to a considerable boost beyond all previous methods, which we named maximize feature distribution variance stochastic neural network (MFDV-SNN). Extensive experiments on well-known white- and black-box attacks show that MFDV-SNN achieves a significant improvement over existing methods, which indicates that it is a simple but effective method to improve model robustness.

cs.LG↗

arXiv2021

Inference on Individual Treatment Effects in Nonseparable Triangular Models

In nonseparable triangular models with a binary endogenous treatment and a binary instrumental variable, Vuong and Xu (2017) established identification results for individual treatment effects (ITEs) under the rank invariance assumption. Using their approach, Feng, Vuong, and Xu (2019) proposed a uniformly consistent kernel estimator for the density of the ITE that utilizes estimated ITEs. In this paper, we establish the asymptotic normality of the density estimator of Feng, Vuong, and Xu (2019) and show that the ITE estimation errors have a non-negligible effect on the asymptotic distribution of the estimator. We propose asymptotically valid standard errors that account for ITEs estimation, as well as a bias correction. Furthermore, we develop uniform confidence bands for the density of the ITE using the jackknife multiplier or nonparametric bootstrap critical values.

econ.EM↗

arXiv2020

Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs

This paper proposes a versatile covariate adjustment method that directly incorporates covariate balance in regression discontinuity (RD) designs. The new empirical entropy balancing method reweights the standard local polynomial RD estimator by using the entropy balancing weights that minimize the Kullback--Leibler divergence from the uniform weights while satisfying the covariate balance constraints. Our estimator can be formulated as an empirical likelihood estimator that efficiently incorporates the information from the covariate balance condition as correctly specified over-identifying moment restrictions, and thus has an asymptotic variance no larger than that of the standard estimator without covariates. We demystify the asymptotic efficiency gain of Calonico, Cattaneo, Farrell, and Titiunik (2019)'s regression-based covariate-adjusted estimator, as their estimator has the same asymptotic variance as ours. Further efficiency improvement from balancing over sieve spaces is possible if our entropy balancing weights are computed using stronger covariate balance constraints that are imposed on functions of covariates. We then show that our method enjoys favorable second-order properties from empirical likelihood estimation and inference: the estimator has a small (bounded) nonlinearity bias, and the likelihood ratio based confidence set admits a simple analytical correction that can be used to improve coverage accuracy. The coverage accuracy of our confidence set is robust against slight perturbation to the covariate balance condition, which may happen in cases such as data contamination and misspecified "unaffected" outcomes used as covariates. The proposed entropy balancing approach for covariate adjustment is applicable to other RD-related settings.

econ.EM↗

arXiv2017

Estimation and Inference on Treatment Effects Under Treatment-Based Sampling

Causal inference in a program evaluation setting faces the problem of external validity when the treatment effect in the target population is different from the treatment effect identified from the population of which the sample is representative. This paper focuses on a situation where such discrepancy arises by a stratified sampling design based on the individual treatment status and other characteristics. In such settings, the design probability is known from the sampling design but the target population depends on the underlying population share vector which is often unknown, and except for special cases, the treatment effect parameters are not identified. In this paper, we propose a method of constructing confidence sets that are valid for a given range of population shares. When a benchmark population share vector and a corresponding estimator of a treatment effect parameter are given, we develop a method to discover the scope of external validity with familywise error rate control. Finally, we derive an optimal sampling design which minimizes the semiparametric efficiency bound given a population share associated with a target population. We provide Monte Carlo simulation results and an empirical application to demonstrate the usefulness of our proposals.

stat.ME↗