arXiv subjects

Liyang Sun

Publications and source records attributed to Liyang Sun.

15 recordsLinked to original sources

arXiv2026

A Practical Guide to Instrumental Variables Methods with Heterogeneous Treatment Effects

Instrumental variables (IV) methods are central to applied microeconomics. While classical approaches assume linear models with constant effects, recent literature has shifted toward the local average treatment effect (LATE) framework to accommodate heterogeneous treatment effects. This paper provides a practical guide to aligning empirical practice with recent theory. We first examine how different specifications with covariates lead to distinct weighted averages of covariate-specific LATEs. We then discuss how parametric misspecification can undermine the causal interpretation of these estimands and suggest flexible specifications as essential robustness checks. Finally, we review formal tests for LATE assumptions and methods robust to monotonicity violations. We provide a guide to software implementations to help researchers apply the methods in practice.

econ.EM↗

arXiv2025

Compound Selection Decisions: An Almost SURE Approach

This paper proposes methods for producing compound selection decisions in a Gaussian sequence model. Given unknown, fixed parameters $\mu_ {1:n}$ and known $\sigma_{1:n}$ with observations $Y_i \sim \textsf{N}(\mu_i, \sigma_i^2)$, the decision maker would like to select a subset of indices $S$ so as to maximize utility $\frac{1}{n}\sum_{i\in S} (\mu_i - K_i)$, for known costs $K_i$. Inspired by Stein's unbiased risk estimate (SURE), we introduce an almost unbiased estimator, called ASSURE, for the expected utility of a proposed decision rule. ASSURE allows a user to choose a welfare-maximizing rule from a pre-specified class by optimizing the estimated welfare, thereby producing selection decisions that borrow strength across noisy estimates. We show that ASSURE produces decision rules that are asymptotically no worse than the optimal but infeasible decision rule in the pre-specified class. We apply ASSURE to the selection of Census tracts for economic opportunity, the identification of discriminating firms, and the analysis of $p$-value decision procedures in A/B testing.

econ.EM↗

arXiv2025

Estimating Treatment Effects Under Bounded Heterogeneity

Specifications that impose constant treatment effects are common and can be biased under heterogeneity, whereas fully flexible alternatives can be imprecise or infeasible. Under a bound on the target-weighted variance of treatment effects, this paper proposes a generalized ridge estimator, $\texttt{regulaTE}$, that yields heterogeneity-aware confidence intervals (CIs). The ridge penalty is chosen to minimize the heterogeneity-aware CI length in a homoskedastic Gaussian setting; the resulting CIs are asymptotically valid even when overlap fails. Varying the bound enables sensitivity analysis of departures from constant effects, illustrated in applications to unconfoundedness and staggered adoption designs.

econ.EM↗

arXiv2025

Policy Learning with Confidence

This paper introduces a rule for policy selection in the presence of estimation uncertainty, explicitly accounting for estimation risk. The rule belongs to the class of risk-aware rules on the efficient decision frontier, characterized as policies offering maximal estimated welfare for a given level of estimation risk. Among this class, the proposed rule is chosen to provide a reporting guarantee, ensuring that the welfare delivered exceeds a threshold with a pre-specified confidence level. We apply this approach to the allocation of a limited budget among social programs using estimates of their marginal value of public funds and associated standard errors.

econ.EM↗

arXiv2024

Temporal Aggregation for the Synthetic Control Method

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit with panel data. Two challenges arise with higher frequency data (e.g., monthly versus yearly): (1) achieving excellent pre-treatment fit is typically more challenging; and (2) overfitting to noise is more likely. Aggregating data over time can mitigate these problems but can also destroy important signal. In this paper, we bound the bias for SCM with disaggregated and aggregated outcomes and give conditions under which aggregating tightens the bounds. We then propose finding weights that balance both disaggregated and aggregated series.

econ.EM↗

arXiv2024

Interactive $360^{\circ}$ Video Streaming Using FoV-Adaptive Coding with Temporal Prediction

For $360^{\circ}$ video streaming, FoV-adaptive coding that allocates more bits for the predicted user's field of view (FoV) is an effective way to maximize the rendered video quality under the limited bandwidth. We develop a low-latency FoV-adaptive coding and streaming system for interactive applications that is robust to bandwidth variations and FoV prediction errors. To minimize the end-to-end delay and yet maximize the coding efficiency, we propose a frame-level FoV-adaptive inter-coding structure. In each frame, regions that are in or near the predicted FoV are coded using temporal and spatial prediction, while a small rotating region is coded with spatial prediction only. This rotating intra region periodically refreshes the entire frame, thereby providing robustness to both FoV prediction errors and frame losses due to transmission errors. The system adapts the sizes and rates of different regions for each video segment to maximize the rendered video quality under the predicted bandwidth constraint. Integrating such frame-level FoV adaptation with temporal prediction is challenging due to the temporal variations of the FoV. We propose novel ways for modeling the influence of FoV dynamics on the quality-rate performance of temporal predictive coding.We further develop LSTM-based machine learning models to predict the user's FoV and network bandwidth.The proposed system is compared with three benchmark systems, using real-world network bandwidth traces and FoV traces, and is shown to significantly improve the rendered video quality, while achieving very low end-to-end delay and low frame-freeze probability.

eess.IV↗

arXiv2024

Coffee: Cost-Effective Edge Caching for 360 Degree Live Video Streaming

While live 360 degree video streaming delivers immersive viewing experience, it poses significant bandwidth and latency challenges for content delivery networks. Edge servers are expected to play an important role in facilitating live streaming of 360 degree videos. In this paper, we propose a novel predictive edge caching algorithm (Coffee) for live 360 degree video that employ collaborative FoV prediction and predictive tile prefetching to reduce bandwidth consumption, streaming cost and improve the streaming quality and robustness. Our light-weight caching algorithms exploit the unique tile consumption patterns of live 360 degree video streaming to achieve high tile caching gains. Through extensive experiments driven by real 360 degree video streaming traces, we demonstrate that edge caching algorithms specifically designed for live 360 degree video streaming can achieve high streaming cost savings with small edge cache space consumption. Coffee, guided by viewer FoV predictions, significantly reduces back-haul traffic up to 76% compared to state-of-the-art edge caching algorithms. Furthermore, we develop a transcoding-aware variant (TransCoffee) and evaluate it using comprehensive experiments, which demonstrate that TransCoffee can achieve 63\% lower cost compared to state-of-the-art transcoding-aware approaches.

cs.MM↗

arXiv2024

Weak Identification with Many Instruments

Linear instrumental variable regressions are widely used to estimate causal effects. Many instruments arise from the use of ``technical'' instruments and more recently from the empirical strategy of ``judge design''. This paper surveys and summarizes ideas from recent literature on estimation and statistical inferences with many instruments for a single endogenous regressor. We discuss how to assess the strength of the instruments and how to conduct weak identification-robust inference under heteroskedasticity. We establish new results for a jack-knifed version of the Lagrange Multiplier (LM) test statistic. Furthermore, we extend the weak-identification-robust tests to settings with both many exogenous regressors and many instruments. We propose a test that properly partials out many exogenous regressors while preserving the re-centering property of the jack-knife. The proposed tests have correct size and good power properties.

econ.EM↗

arXiv2023

Using Multiple Outcomes to Improve the Synthetic Control Method

When there are multiple outcome series of interest, Synthetic Control analyses typically proceed by estimating separate weights for each outcome. In this paper, we instead propose estimating a common set of weights across outcomes, by balancing either a vector of all outcomes or an index or average of them. Under a low-rank factor model, we show that these approaches lead to lower bias bounds than separate weights, and that averaging leads to further gains when the number of outcomes grows. We illustrate this via a re-analysis of the impact of the Flint water crisis on educational outcomes.

econ.EM↗

arXiv2023

Adapting to Misspecification

Empirical research typically involves a robustness-efficiency tradeoff. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased, or they can relax some of these assumptions to motivate a more robust, but variable, unrestricted estimator. When a bound on the bias of the restricted estimator is available, it is optimal to shrink the unrestricted estimator towards the restricted estimator. For settings where a bound on the bias of the restricted estimator is unknown, we propose adaptive estimators that minimize the percentage increase in worst case risk relative to an oracle that knows the bound. We show that adaptive estimators solve a weighted convex minimax problem and provide lookup tables facilitating their rapid computation. Revisiting some well known empirical studies where questions of model specification arise, we examine the advantages of adapting to -- rather than testing for -- misspecification.

econ.EM↗

arXiv2022

Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics

We propose a semiparametric test to evaluate (i) whether different instruments induce subpopulations of compliers with the same observable characteristics on average, and (ii) whether compliers have observable characteristics that are the same as the full population on average. The test is a flexible robustness check for the external validity of instruments. We use it to reinterpret the difference in LATE estimates that Angrist and Evans (1998) obtain when using different instrumental variables. To justify the test, we characterize the doubly robust moment for Abadie (2003)'s class of complier parameters, and we analyze a machine learning update to $κ$ weighting.

stat.ML↗

arXiv2021

Inference with Many Weak Instruments

We develop a concept of weak identification in linear IV models in which the number of instruments can grow at the same rate or slower than the sample size. We propose a jackknifed version of the classical weak identification-robust Anderson-Rubin (AR) test statistic. Large-sample inference based on the jackknifed AR is valid under heteroscedasticity and weak identification. The feasible version of this statistic uses a novel variance estimator. The test has uniformly correct size and good power properties. We also develop a pre-test for weak identification that is related to the size property of a Wald test based on the Jackknife Instrumental Variable Estimator (JIVE). This new pre-test is valid under heteroscedasticity and with many instruments.

econ.EM↗

arXiv2021

Empirical Welfare Maximization with Constraints

Empirical Welfare Maximization (EWM) is a framework that can be used to select welfare program eligibility policies based on data. This paper extends EWM by allowing for uncertainty in estimating the budget needed to implement the selected policy, in addition to its welfare. Due to the additional estimation error, I show there exist no rules that achieve the highest welfare possible while satisfying a budget constraint uniformly over a wide range of DGPs. This differs from the setting without a budget constraint where uniformity is achievable. I propose an alternative trade-off rule and illustrate it with Medicaid expansion, a setting with imperfect take-up and varying program costs.

econ.EM↗

arXiv2020

Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects

To estimate the dynamic effects of an absorbing treatment, researchers often use two-way fixed effects regressions that include leads and lags of the treatment. We show that in settings with variation in treatment timing across units, the coefficient on a given lead or lag can be contaminated by effects from other periods, and apparent pretrends can arise solely from treatment effects heterogeneity. We propose an alternative estimator that is free of contamination, and illustrate the relative shortcomings of two-way fixed effects regressions with leads and lags through an empirical application.

econ.EM↗

arXiv2017

Multipath IP Routing on End Devices: Motivation, Design, and Performance

Most end devices are now equipped with multiple network interfaces. Applications can exploit all available interfaces and benefit from multipath transmission. Recently Multipath TCP (MPTCP) was proposed to implement multipath transmission at the transport layer and has attracted lots of attention from academia and industry. However, MPTCP only supports TCP-based applications and its multipath routing flexibility is limited. In this paper, we investigate the possibility of orchestrating multipath transmission from the network layer of end devices, and develop a Multipath IP (MPIP) design consisting of signaling, session and path management, multipath routing, and NAT traversal. We implement MPIP in Linux and Android kernels. Through controlled lab experiments and Internet experiments, we demonstrate that MPIP can effectively achieve multipath gains at the network layer. It not only supports the legacy TCP and UDP protocols, but also works seamlessly with MPTCP. By facilitating user-defined customized routing, MPIP can route traffic from competing applications in a coordinated fashion to maximize the aggregate user Quality-of-Experience.

cs.NI↗