Sharp Bounds on Treatment Effects for Policy Evaluation

For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to the policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This paper investigates the possibility of extrapolating local treatment effects to different counterfactual settings when instrumental variables are only binary. We propose a novel framework to systematically calculate sharp nonparametric bounds on various policy-relevant treatment parameters that are defined as weighted averages of the marginal treatment effect (MTE). Our framework is flexible enough to incorporate a large menu of identifying assumptions beyond the shape restrictions on the MTE that have been considered in prior studies. We apply our method to understand the effects of medical insurance policies on the use of medical services

Essays on nonparametric and semiparametric identification and estimation

This dissertation consists of three chapters in econometric theory, with a focus on identification and estimation of treatment effect in semi-parametric and nonparametric models, when there exists endogeneity problem. These methods are applied on policy and program evaluation in health and labor economics. \indent In the first chapter, I examine the common problem of multiple missing variables, which we refer to as multiple missingness, with non-monotone missing pattern and is usually caused by sub-sampling and a combination of different data sets. One example of this is missingness in both the endogenous treatment and outcome when two variables are collected via different stages of follow-up surveys. Two types of dependence assumptions for multiple missingness are proposed to identify the missing mechanism. The identified missing mechanisms are used later in an Augmented Inverse Propensity Weighted moment function, based on which a two-step semiparametric GMM estimator of the coefficients in the primary model is proposed. This estimator is consistent and more efficient than the previously used estimation methods because it includes incomplete observations. We demonstrate that robustness and asymptotic variances differ under two sets of identification assumptions, and we determine sufficient conditions when the proposed estimator can achieve the semiparametric efficiency bound. This method is applied to the Oregon Health Insurance Experiment and shows the significant effects of enrolling in the Oregon Health Plan on improving health-related outcomes and reducing out-of-pocket costs for medical care. The method proposed here provides unbiased and more efficient estimates. There is evidence that simply dropping the incomplete data creates downward biases for some of the chosen outcome variables. Moreover, the estimator proposed in this paper reduced standard errors by 6-24% of the estimated effects of the Oregon Health Plan. \indent The second chapter is a joint work with Sukjin Han. In this chapter, we consider how to extrapolate the general local treatment effect in a non-parametric setting, with endogenous self-selection problem and lack of external validity. For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to the policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This chapter investigates the possibility of extrapolating local treatment effects to different counterfactual settings when instrumental variables are only binary. We propose a novel framework to systematically calculate sharp nonparametric bounds on various policy-relevant treatment parameters that are defined as weighted averages of the marginal treatment effect (MTE). Our framework is flexible enough to incorporate a large menu of identifying assumptions beyond the shape restrictions on the MTE that have been considered in prior studies. We apply our method to understand the effects of medical insurance policies on the use of medical services. \indent In the third chapter, I investigate the partial identification bound for treatment effect in a dynamic setting. First, I develop the sharp partial identification bounds of dynamic treatment effect on conditional transition probabilities when the treatment is randomly assigned. Then I relax the randomization assumption and gives partial identification bounds, under a conditional mean independence assumption. Using MTR and MTS assumptions, this bound is further tightened. These bounds are used on estimating labor market return of college degree in a long term, with data from NLSY79.Economic

The Phenomenological Research on Higgs and dark matter in the Next-to-Minimal Supersymmetric Standard Model

The $Z_3$-invariant next-to-minimal supersymmetric standard model (NMSSM) can provide a candidate for dark matter (DM). It can also be used to explain the hypothesis that the Higgs signal observed on the Large Hadron Collider (LHC) comes from the contribution of the two lightest CP-even Higgs bosons, whose masses are near 125 GeV. At present, XENON1T, LUX, and PandaX experiments have imposed very strict restrictions on direct collision cross sections of {dark matter}. In this paper, we consider a scenario that the observed Higgs signal is the superposition of two mass-degenerate Higgs in the $Z_3$-invariant NMSSM and scan the seven-dimension parameter space composing of $\lambda, \kappa, \tan\beta, \mu, A_k, A_t, M_1$ via the Markov chain Monte Carlo (MCMC) method. We find that the DM relic density, as well as the LHC searches for sparticles, especially the DM direct detections, has provided a strong limit on the parameter space. %Please check intended meaning has been retained. The allowed parameter space is featured by a relatively small $\mu \le 300$ GeV and about $\tan\beta\in(10,20)$. In addition, the DM is Higgsino-dominated because of $|\frac{2\kappa}{\lambda}|>1$. Moreover, the co-annihilation between $\tilde{\chi}_1^0$ and $\tilde{\chi}_1^\pm$ must be taken into account to obtain the reasonable DM relic density

Fast Inverse Model Transformation: Algebraic Framework for Fast Data Plane Verification

Data plane verification (DPV) analyzes routing tables and detects routing abnormalities and policy violations during network operation and planning. Thus, it has become an important tool to harden the networking infrastructure and the computing systems building on top. Substantial advancements have been made in the last decade and state-of-the-art DPV systems can achieve sub-us verification for an update of a single forwarding rule. In this paper, we introduce fast inverse model transformation (FIMT), the first theoretical framework to systematically model and analyze centralized DPV systems. FIMT reveals the algebraic structure in the model update process, a key step in fast DPV systems. Thus, it can systematically analyze the correctness of several DPV systems, using algebraic properties. The theory also guides the design and implementation of NeoFlash, a refactored version of Flash with new optimization techniques. Evaluations show that NeoFlash outperforms existing state-of-the-art centralized DPV systems in various datasets and reveal insights to key techniques towards fast DPV.Comment: 12 pages pre-referenc

Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO

In order to protect computing systems from malicious attacks, network intrusion detection systems have become an important part in the security infrastructure. Recently, hybrid models that integrating several machine learning techniques have captured more attention of researchers. In this paper, a novel hybrid model was proposed with the purpose of detecting network intrusion effectively. In the proposed model, Gini index is used to select the optimal subset of features, the gradient boosted decision tree (GBDT) algorithm is adopted to detect network attacks, and the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of GBDT. The performance of the proposed model is experimentally evaluated in terms of accuracy, detection rate, precision, F1-score, and false alarm rate using the NSL-KDD dataset. Experimental results show that the proposed model is superior to the compared methods

Six-month adherence to Statin use and subsequent risk of major adverse cardiovascular events (MACE) in patients discharged with acute coronary syndromes

Acknowledgements: The authors thank all participants who contributed to the study. Funding: CPACS-1 was funded by unrestricted educational grants from Guidant and Sanofi-Aventis, and grants from The Royal Australasian College of Physicians. AP is supported by an Australian National Heart Foundation Career Development Award. CPACS-2 was funded by an unrestricted grant from Sanofi-Aventis China. The George Institute for Global Health at Peking University Health Science Center sponsored the study and owns the data. Data analyses and reports were supported by Beijing Science and Technology Key Research Plan (D151100002215001). However, the authors are solely responsible for the design, analyses, the drafting and editing of the manuscript, and its final contents.Peer reviewedPublisher PD

Random forest model based fine scale spatiotemporal O₃ trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017

Ambient ozone (O₃) concentrations have shown an upward trend in China and its health hazards have also been recognized in recent years. High-resolution exposure data based on statistical models are needed. Our study aimed to build high-performance random forest (RF) models based on training data from 2013 to 2017 in the Beijing-Tianjin-Hebei (BTH) region in China at a 0.01 ° × 0.01 ° resolution, and estimated daily maximum 8h average O₃ (O₃-8hmax) concentration, daily average O₃ (O₃-mean) concentration, and daily maximum 1h O3 (O3-1hmax) concentration from 2010 to 2017. Model features included meteorological variables, chemical transport model output variables, geographic variables, and population data. The test-R² of sample-based O₃-8hmax, O₃-mean and O₃-1hmax models were all greater than 0.80, while the R² of site-based and date-based model were 0.68–0.87. From 2010 to 2017, O₃-8hmax, O₃-mean, and O₃-1hmax concentrations in the BTH region increased by 4.18 μg/m³, 0.11 μg/m³, and 4.71 μg/m³, especially in more developed regions. Due to the influence of weather conditions, which showed high contribution to the model, the long-term spatial distribution of O₃ concentrations indicated a similar pattern as altitude, where high concentration levels were distributed in regions with higher altitude