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Haitian Xie

Publications and source records attributed to Haitian Xie.

At least 19 recordsLinked to original sources

Improving on a Lottery: Efficient Estimation of Optimal Assignment Rules

Scarce opportunities are often allocated by lotteries. We study how to improve such allocations by estimating optimal assignment rules that maximize welfare net of a Kullback--Leibler penalty for departing from the benchmark randomization. The framework covers discrete, continuous, and mixed treatments. Regret is asymptotically quadratic in the estimation error, so inefficient estimation raises the mean of limiting regret, not merely its dispersion. We show that inverse probability weighting with known assignment probabilities is inefficient, whereas estimated-propensity and doubly robust welfare criteria attain the efficient regret distribution. Simulations and a commitment-savings application quantify the resulting precision gains.

econ.EM

On Local Overidentification and Efficiency Gains in Modern Causal Inference and Data Combination

This paper studies nonparametric local (over-)identification and the semiparametric efficiency in modern causal frameworks. We develop a unified approach that begins by translating structural models with latent variables into their induced statistical models of observables and then analyzes local overidentification through conditional moment restrictions. We apply this approach to three popular classes of causal models: (1) the general treatment model under unconfoundedness; (2) the negative control model, and (3) the long-term causal inference model under unobserved confounding. The first model yields a locally just-identified statistical model, implying that all regular asymptotically linear estimators of the treatment effect have the same asymptotic variance, which equals the (trivial) semiparametric efficient variance bound. In contrast, the latter two models involve nonparametric endogeneity and are naturally locally overidentified; consequently, some doubly robust orthogonal moment estimators of the average treatment effect are inefficient. Whereas existing work typically imposes strong conditions to restore local just-identification to justify the efficiency of their doubly robust orthogonal moment estimators, we characterize the semiparametric efficient variance bounds, along with efficient estimators, for the (locally) overidentified models (2) and (3). A small real data application, along with a simulation study, illustrates the semiparametric efficiency gains in model (3).

econ.EM

Data-Driven Policy Learning for Continuous Treatments

This paper studies policy learning for continuous treatments from observational data. Continuous treatments present more significant challenges than discrete ones because population welfare may need nonparametric estimation, and policy space may be infinite-dimensional and may satisfy shape restrictions. We propose to approximate the policy space with a sequence of finite-dimensional spaces and, for any given policy, obtain the empirical welfare by applying the kernel method. We consider two cases: known and unknown propensity scores. In the latter case, we allow for machine learning of the propensity score and modify the empirical welfare to account for the effect of machine learning. The learned policy maximizes the empirical welfare or the modified empirical welfare over the approximating space. In both cases, we modify the penalty algorithm proposed in Mbakop and Tabord-Meehan (2021) to data-automate the tuning parameters (i.e., bandwidth and dimension of the approximating space) and establish an oracle inequality for the welfare regret.

econ.EM

Difference-in-Differences Meets Synthetic Control: Doubly Robust Identification and Estimation

Difference-in-Differences (DiD) and Synthetic Control (SC) are widely used methods for causal inference in panel data, each with distinct strengths and limitations. We propose a novel method for short-panel causal inference that integrates the advantages of both approaches. Our method delivers a doubly robust identification strategy for the average treatment effect on the treated (ATT) under either of two non-nested assumptions: parallel trends or a group-level SC condition. Building on this identification result, we develop a unified semiparametric framework for estimating the ATT. Notably, the identification-robust moment function satisfies Neyman orthogonality under the parallel trends assumption but not under the SC assumption, leading to different asymptotic variances across the two identification strategies. To ensure valid inference, we propose a multiplier bootstrap method that consistently approximates the asymptotic distribution under either assumption. Furthermore, we extend our methodology to accommodate repeated cross-sectional data and staggered treatment designs. As an empirical application, we evaluate the impact of the 2003 minimum wage increase in Alaska on family income. Finally, in simulation studies based on empirically calibrated data-generating processes, we demonstrate that the proposed estimation and inference methods perform well in finite samples under either identification assumption.

econ.EM

Efficient Difference-in-Differences and Event Study Estimators

This paper investigates efficient Difference-in-Differences (DiD) and Event Study (ES) estimation using short panel data sets within the heterogeneous treatment effect framework, free from parametric functional form assumptions and allowing for variation in treatment timing. We provide an equivalent characterization of the DiD potential outcome model using sequential conditional moment restrictions on observables, which shows that the DiD identification assumptions typically imply nonparametric overidentification restrictions. We derive the semiparametric efficient influence function (EIF) in closed form for DiD and ES causal parameters under commonly imposed parallel trends assumptions. The EIF is automatically Neyman orthogonal and yields the smallest variance among all asymptotically normal, regular estimators of the DiD and ES parameters. Leveraging the EIF, we propose simple-to-compute efficient estimators. Our results highlight how to optimally explore different pre-treatment periods and comparison groups to obtain the tightest (asymptotic) confidence intervals, offering practical tools for improving inference in modern DiD and ES applications even in small samples. Calibrated simulations and an empirical application demonstrate substantial precision gains of our efficient estimators in finite samples.

econ.EM

A Synthetic Business Cycle Approach to Counterfactual Analysis with Nonstationary Macroeconomic Data

This paper investigates the use of synthetic control methods for causal inference in macroeconomic settings when dealing with possibly nonstationary data. While the synthetic control approach has gained popularity for estimating counterfactual outcomes, we caution researchers against assuming a common nonstationary trend factor across units for macroeconomic outcomes, as doing so may result in misleading causal estimation-a pitfall we refer to as the spurious synthetic control problem. To address this issue, we propose a synthetic business cycle framework that explicitly separates trend and cyclical components. By leveraging the treated unit's historical data to forecast its trend and using control units only for cyclical fluctuations, our divide-and-conquer strategy eliminates spurious correlations and improves the robustness of counterfactual prediction in macroeconomic applications. As empirical illustrations, we examine the cases of German reunification and the handover of Hong Kong, demonstrating the advantages of the proposed approach.

econ.EM

Electromagnetic Duality for Line Defect Correlators in $\mathcal{N}=4$ Super Yang-Mills Theory

We study particular integrated correlation functions of two superconformal primary operators of the stress tensor multiplet in the presence of a half-BPS line defect labelled by electromagnetic charges $(p,q)$ in $\mathcal{N}=4$ supersymmetric Yang-Mills theory (SYM) with gauge group $SU(N)$. An important consequence of ${\rm SL}(2,\mathbb{Z})$ electromagnetic duality in $\mathcal{N}=4$ SYM is that correlators of line defect operators with different charges $(p,q)$ must be related in a non-trivial manner when the complex coupling $τ=θ/(2π)+4πi /g_{_{\rm YM}}^2$ is transformed appropriately. In this work we introduce a novel class of real-analytic functions whose automorphic properties with respect to ${\rm SL}(2,\mathbb{Z})$ match the expected transformations of line defect operators in $\mathcal{N}=4$ SYM under electromagnetic duality. At large $N$ and fixed $τ$, the correlation functions we consider are related to scattering amplitudes of two gravitons from extended $(p,q)$-strings in the holographic dual type IIB superstring theory. We show that the large-$N$ expansion coefficients of the integrated two-point line defect correlators are given by finite linear combinations with rational coefficients of elements belonging to this class of automorphic functions. On the other hand, for any fixed value of $N$ we conjecture that the line defect integrated correlators can be expressed as formal infinite series over such automorphic functions. The resummation of this series produces a simple lattice sum representation for the integrated line defect correlator that manifests its automorphic properties. We explicitly demonstrate this construction for the cases with gauge group $SU(2)$ and $SU(3)$. Our results give direct access to non-perturbative integrated correlators in the presence of an 't Hooft-line defect, observables otherwise very difficult to compute by other means.

hep-th

On the limitations of data-based price discrimination

The classic third degree price discrimination (3PD) model requires the knowledge of the distribution of buyer valuations and the covariate to set the price conditioned on the covariate. In terms of generating revenue, the classic result shows that 3PD is at least as good as uniform pricing. What if the seller has to set a price based only on a sample of observations from the underlying distribution? Is it still obvious that the seller should engage in 3PD? This paper sheds light on these fundamental questions. In particular, the comparison of the revenue performance between 3PD and uniform pricing is ambiguous overall when prices are set based on samples. This finding is in the nature of statistical learning under uncertainty: a curse of dimensionality, but also other small sample complications.

cs.GT

Integrated correlators in $\mathcal{N}=4$ SYM beyond localisation

We study integrated correlators of four superconformal primaries $\mathcal{O}_{p}$ with arbitrary charges $p$ in $\mathcal{N}{=}4$ super Yang-Mills theory (SYM). The $ \langle \mathcal{O}_{2} \mathcal{O}_{2} \mathcal{O}_{p} \mathcal{O}_{p} \rangle$ integrated correlators can be computed by supersymmetric localisation, whereas correlators with more general charges are currently not accessible from this method and in general contain complicated multi-zeta values. Nevertheless we observe that if one sums over the contributions from all different channels in a given correlator, then all the multi-zeta values (and products of zeta's) cancel leaving only $ζ(2\ell{+}1)$ at $\ell$-loops. We then propose an exact expression of such integrated correlators in the planar limit, valid for arbitrary 't Hooft coupling. The expression matches with the known exact localisation-based results for specific charges, as well as with all existing perturbative and strong-coupling results in the literature for more general charges. As an application, our result is used to determine certain $7$-loop Feynman integral periods and fix previously unknown coefficients in the correlators at strong coupling.

hep-th

Employer Reputation and the Labor Market: Evidence from Glassdoor.com and Dice.com

How does employer reputation affect the labor market? We investigate this question using a novel dataset combining reviews from Glassdoor.com and job applications data from Dice.com. Labor market institutions such as Glassdoor.com crowd-sources information about employers to alleviate information problems faced by workers when choosing an employer. Raw crowd-sourced employer ratings are rounded when displayed to job seekers. By exploiting the rounding threshold, we identify the causal impact of Glassdoor ratings using a regression discontinuity framework. We document the effects of such ratings on both the demand and supply sides of the labor market. We find that displayed employer reputation affects an employer's ability to attract workers, especially when the displayed rating is "sticky." Employers respond to having a rating above the rounding threshold by posting more new positions and re-activating more job postings. The effects are the strongest for private, smaller, and less established firms, suggesting that online reputation is a substitute for other types of reputation.

econ.GN

Generating functions and large-charge expansion of integrated correlators in $\mathcal{N}=4$ supersymmetric Yang-Mills theory

We recently proved that, when integrating out spacetime dependence with a certain measure, four-point correlators $\langle \mathcal{O}_2\mathcal{O}_2\mathcal{O}^{(i)}_p \mathcal{O}^{(j)}_p \rangle$ in $SU(N)$ $\mathcal{N}=4$ super Yang-Mills are governed by a universal Laplace-difference equation. Here $\mathcal{O}^{(i)}_p$ is a superconformal primary with charge $p$ and degeneracy $i$. These observables, called integrated correlators, are modular functions of coupling $τ$. The Laplace-difference equation relates integrated correlators of different charges recursively. In this paper, we introduce generating functions for the integrated correlators that sum over the charge. By utilising the Laplace-difference equation, we determine the generating functions given initial data. We show that the transseries of the integrated correlators in the large-$p$ (large-charge) expansion consists of three parts: 1) is independent of $τ$ as a power series in $1/p$, plus an additional $\log(p)$ term if $i=j$; 2) is a power series in $1/p$, with coefficients given by a sum of the non-holomorphic Eisenstein series; 3) is a sum of exponentially decayed modular functions in the large-$p$ limit, which can be viewed as a generalisation of the non-holomorphic Eisenstein series. When $i=j$, there is an additional modular function that is independent of $p$ and is determined by the integrated correlator with $p=2$. The Laplace-difference equation was obtained with a reorganisation of the operators that means the large-charge limit is taken in a particular way here. From the $SL(2,\mathbb{Z})$-invariant results, we also determine the generalised 't Hooft genus expansion and associated large-$p$ non-perturbative corrections of the integrated correlators by introducing $λ= pg^2_{YM}$. The generating functions have subtle differences between even and odd $N$ with important consequences in resurgence.

hep-th

Laplace-difference equation for integrated correlators of operators with general charges in $\mathcal{N}=4$ SYM

We consider the integrated correlators associated with four-point correlation functions $\langle \mathcal{O}_2\mathcal{O}_2\mathcal{O}^{(i)}_p \mathcal{O}^{(j)}_p \rangle$ in four-dimensional $\mathcal{N}=4$ supersymmetric Yang-Mills theory (SYM) with $SU(N)$ gauge group, where $\mathcal{O}^{(i)}_p$ is a superconformal primary with charge (or dimension) $p$ and the superscript $i$ represents possible degeneracy. These integrated correlators are defined by integrating out spacetime dependence with a certain integration measure, and they can be computed via supersymmetric localisation. They are modular functions of complexified Yang-Mills coupling $τ$. We show that the localisation computation is systematised by appropriately reorganising the operators. After this reorganisation of the operators, we prove that all the integrated correlators for any $N$, with some crucial normalisation factor, satisfy a universal Laplace-difference equation (with the laplacian defined on the $τ$-plane) that relates integrated correlators of operators with different charges. This Laplace-difference equation is a recursion relation that completely determines all the integrated correlators, once the initial conditions are given.

hep-th

Uniform Convergence Results for the Local Linear Regression Estimation of the Conditional Distribution

This paper examines the local linear regression (LLR) estimate of the conditional distribution function $F(y|x)$. We derive three uniform convergence results: the uniform bias expansion, the uniform convergence rate, and the uniform asymptotic linear representation. The uniformity in the above results is with respect to both $x$ and $y$ and therefore has not previously been addressed in the literature on local polynomial regression. Such uniform convergence results are especially useful when the conditional distribution estimator is the first stage of a semiparametric estimator. We demonstrate the usefulness of these uniform results with two examples: the stochastic equicontinuity condition in $y$, and the estimation of the integrated conditional distribution function.

econ.EM

Strength in Numbers: Robust Mechanisms for Public Goods with Many Agents

This study examines the mechanism design problem for public goods provision in a large economy with $n$ independent agents. We propose a class of dominant-strategy incentive compatible and ex-post individually rational mechanisms, which we call the adjusted mean-thresholding (AMT) mechanisms. We show that when the cost of provision grows slower than the $\sqrt{n}$-rate, the AMT mechanisms are both eventually ex-ante budget balanced and asymptotically efficient. When the cost grows faster than the $\sqrt{n}$-rate, in contrast, we show that any incentive compatible, individually rational, and eventually ex-ante budget balanced mechanism must have provision probability converging to zero and hence cannot be asymptotically efficient. The AMT mechanisms have a simple form and are more informationally robust when compared to, for example, the second-best mechanism. This is because the construction of an AMT mechanism depends only on the first moment of the valuation distribution.

econ.TH

Grenander-type Density Estimation under Myerson Regularity

This study presents a novel approach to the density estimation of private values from second-price auctions, diverging from the conventional use of smoothing-based estimators. We introduce a Grenander-type estimator, constructed based on a shape restriction in the form of a convexity constraint. This constraint corresponds to the renowned Myerson regularity condition in auction theory, which is equivalent to the concavity of the revenue function for selling the auction item. Our estimator is nonparametric and does not require any tuning parameters. Under mild assumptions, we establish the cube-root consistency and show that the estimator asymptotically follows the scaled Chernoff's distribution. Moreover, we demonstrate that the estimator achieves the minimax optimal convergence rate.

econ.EM

Modular-invariant large-$N$ completion of an integrated correlator in $\mathcal{N}=4$ supersymmetric Yang-Mills theory

The use of supersymmetric localisation has recently led to modular covariant expressions for certain integrated correlators of half-BPS operators in $\mathcal{N} = 4$ supersymmetric Yang-Mills theory with a general classical gauge group $G_N$. Here we determine generating functions that encode such integrated correlators for any classical gauge group and provide a proof of previous conjectured formulae. This gives a systematic understanding of the relation between properties of these correlators at finite $N$ and their expansions at large $N$. In particular, it determines a duality-invariant non-perturbative completion of the large-$N$ expansion in terms of a sum of novel non-holomorphic modular functions. These functions are exponentially suppressed at large $N$ and have the form of a sum of contributions from coincident $(p, q)$-string world-sheet instantons.

hep-th

Nonlinear and Nonseparable Structural Functions in Fuzzy Regression Discontinuity Designs

Many empirical examples of regression discontinuity (RD) designs concern a continuous treatment variable, but the theoretical aspects of such models are less studied. This study examines the identification and estimation of the structural function in fuzzy RD designs with a continuous treatment variable. The structural function fully describes the causal impact of the treatment on the outcome. We show that the nonlinear and nonseparable structural function can be nonparametrically identified at the RD cutoff under shape restrictions, including monotonicity and smoothness conditions. Based on the nonparametric identification equation, we propose a three-step semiparametric estimation procedure and establish the asymptotic normality of the estimator. The semiparametric estimator achieves the same convergence rate as in the case of a binary treatment variable. As an application of the method, we estimate the causal effect of sleep time on health status by using the discontinuity in natural light timing at time zone boundaries.

econ.EM

Global Representation of the Conditional LATE Model: A Separability Result

This paper studies the latent index representation of the conditional LATE model, making explicit the role of covariates in treatment selection. We find that if the directions of the monotonicity condition are the same across all values of the conditioning covariate, which is often assumed in the literature, then the treatment choice equation has to satisfy a separability condition between the instrument and the covariate. This global representation result establishes testable restrictions imposed on the way covariates enter the treatment choice equation. We later extend the representation theorem to incorporate multiple ordered levels of treatment.

econ.EM