pwc-archive/papers-with-abstracts huggingface dataset:
Explainable AI in Spatial Analysis
Authors: ZiQi Li
Date: 2025-05-01 00:00:00
Tasks: Bias Detection, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

This chapter discusses the opportunities of eXplainable Artificial Intelligence (XAI) within the realm of spatial analysis. A key objective in spatial analysis is to model spatial relationships and infer spatial processes to generate knowledge from spatial data, which has been largely based on spatial statistical methods. More recently, machine learning offers scalable and flexible approaches that complement traditional methods and has been increasingly applied in spatial data science. Despite its advantages, machine learning is often criticized for being a black box, which limits our understanding of model behavior and output. Recognizing this limitation, XAI has emerged as a pivotal field in AI that provides methods to explain the output of machine learning models to enhance transparency and understanding. These methods are crucial for model diagnosis, bias detection, and ensuring the reliability of results obtained from machine learning models. This chapter introduces key concepts and methods in XAI with a focus on Shapley value-based approaches, which is arguably the most popular XAI method, and their integration with spatial analysis. An empirical example of county-level voting behaviors in the 2020 Presidential election is presented to demonstrate the use of Shapley values and spatial analysis with a comparison to multi-scale geographically weighted regression. The chapter concludes with a discussion on the challenges and limitations of current XAI techniques and proposes new directions.
Pre-Training Estimators for Structural Models: Application to Consumer Search
Authors: Yanhao 'Max' Wei, Zhenling Jiang
Date: 2025-05-01 00:00:00

We explore pretraining estimators for structural econometric models. The estimator is "pretrained" in the sense that the bulk of the computational cost and researcher effort occur during the construction of the estimator. Subsequent applications of the estimator to different datasets require little computational cost or researcher effort. The estimation leverages a neural net to recognize the structural model's parameter from data patterns. As an initial trial, this paper builds a pretrained estimator for a sequential search model that is known to be difficult to estimate. We evaluate the pretrained estimator on 12 real datasets. The estimation takes seconds to run and shows high accuracy. We provide the estimator at pnnehome.github.io. More generally, pretrained, off-the-shelf estimators can make structural models more accessible to researchers and practitioners.
A Unifying Framework for Robust and Efficient Inference with Unstructured Data
Authors: Jacob Carlson, Melissa Dell
Date: 2025-05-01 00:00:00
Tasks: Causal Inference, Descriptive

This paper presents a general framework for conducting efficient and robust inference on parameters derived from unstructured data, which include text, images, audio, and video. Economists have long incorporated data extracted from texts and images into their analyses, a practice that has accelerated with advancements in deep neural networks. However, neural networks do not generically produce unbiased predictions, potentially propagating bias to estimators that use their outputs. To address this challenge, we reframe inference with unstructured data as a missing structured data problem, where structured data are imputed from unstructured inputs using deep neural networks. This perspective allows us to apply classic results from semiparametric inference, yielding valid, efficient, and robust estimators based on unstructured data. We formalize this approach with MARS (Missing At Random Structured Data), a unifying framework that integrates and extends existing methods for debiased inference using machine learning predictions, linking them to a variety of older, familiar problems such as causal inference. We develop robust and efficient estimators for both descriptive and causal estimands and address challenges such as inference using aggregated and transformed predictions from unstructured data. Importantly, MARS applies to common empirical settings that have received limited attention in the existing literature. Finally, we reanalyze prominent studies that use unstructured data, demonstrating the practical value of MARS.
Policy Learning with $α$-Expected Welfare
Authors: Yanqin Fan, Yuan Qi, Gaoqian Xu
Date: 2025-05-01 00:00:00
Tasks: valid

This paper proposes an optimal policy that targets the average welfare of the worst-off $\alpha$-fraction of the post-treatment outcome distribution. We refer to this policy as the $\alpha$-Expected Welfare Maximization ($\alpha$-EWM) rule, where $\alpha \in (0,1]$ denotes the size of the subpopulation of interest. The $\alpha$-EWM rule interpolates between the expected welfare ($\alpha=1$) and the Rawlsian welfare ($\alpha\rightarrow 0$). For $\alpha\in (0,1)$, an $\alpha$-EWM rule can be interpreted as a distributionally robust EWM rule that allows the target population to have a different distribution than the study population. Using the dual formulation of our $\alpha$-expected welfare function, we propose a debiased estimator for the optimal policy and establish its asymptotic upper regret bounds. In addition, we develop asymptotically valid inference for the optimal welfare based on the proposed debiased estimator. We examine the finite sample performance of the debiased estimator and inference via both real and synthetic data.
On the Robustness of Mixture Models in the Presence of Hidden Markov Regimes with Covariate-Dependent Transition Probabilities
Authors: Demian Pouzo, Martin Sola, Zacharias Psaradakis
Date: 2025-04-30 00:00:00

This paper studies the robustness of quasi-maximum-likelihood (QML) estimation in hidden Markov models (HMMs) when the regime-switching structure is misspecified. Specifically, we examine the case where the true data-generating process features a hidden Markov regime sequence with covariate-dependent transition probabilities, but estimation proceeds under a simplified mixture model that assumes regimes are independent and identically distributed. We show that the parameters governing the conditional distribution of the observables can still be consistently estimated under this misspecification, provided certain regularity conditions hold. Our results highlight a practical benefit of using computationally simpler mixture models in settings where regime dependence is complex or difficult to model directly.
Real-time Program Evaluation using Anytime-valid Rank Tests
Authors: Sam van Meer, Nick W. Koning
Date: 2025-04-30 00:00:00
Tasks: counterfactual, valid

Counterfactual mean estimators such as difference-in-differences and synthetic control have grown into workhorse tools for program evaluation. Inference for these estimators is well-developed in settings where all post-treatment data is available at the time of analysis. However, in settings where data arrives sequentially, these tests do not permit real-time inference, as they require a pre-specified sample size T. We introduce real-time inference for program evaluation through anytime-valid rank tests. Our methodology relies on interpreting the absence of a treatment effect as exchangeability of the treatment estimates. We then convert these treatment estimates into sequential ranks, and construct optimal finite-sample valid sequential tests for exchangeability. We illustrate our methods in the context of difference-in-differences and synthetic control. In simulations, they control size even under mild exchangeability violations. While our methods suffer slight power loss at T, they allow for early rejection (before T) and preserve the ability to reject later (after T).
Publication Design with Incentives in Mind
Authors: Ravi Jagadeesan, Davide Viviano
Date: 2025-04-29 00:00:00

The publication process both determines which research receives the most attention, and influences the supply of research through its impact on the researcher's private incentives. We introduce a framework to study optimal publication decisions when researchers can choose (i) whether or how to conduct a study and (ii) whether or how to manipulate the research findings (e.g., via selective reporting or data manipulation). When manipulation is not possible, but research entails substantial private costs for the researchers, it may be optimal to incentivize cheaper research designs even if they are less accurate. When manipulation is possible, it is optimal to publish some manipulated results, as well as results that would have not received attention in the absence of manipulability. Even if it is possible to deter manipulation, such as by requiring pre-registered experiments instead of (potentially manipulable) observational studies, it is suboptimal to do so when experiments entail high research costs.
An Axiomatic Approach to Comparing Sensitivity Parameters
Authors: Paul Diegert, Matthew A. Masten, Alexandre Poirier
Date: 2025-04-29 00:00:00
Tasks: Sensitivity

Many methods are available for assessing the importance of omitted variables. These methods typically make different, non-falsifiable assumptions. Hence the data alone cannot tell us which method is most appropriate. Since it is unreasonable to expect results to be robust against all possible robustness checks, researchers often use methods deemed "interpretable", a subjective criterion with no formal definition. In contrast, we develop the first formal, axiomatic framework for comparing and selecting among these methods. Our framework is analogous to the standard approach for comparing estimators based on their sampling distributions. We propose that sensitivity parameters be selected based on their covariate sampling distributions, a design distribution of parameter values induced by an assumption on how covariates are assigned to be observed or unobserved. Using this idea, we define a new concept of parameter consistency, and argue that a reasonable sensitivity parameter should be consistent. We prove that the literature's most popular approach is inconsistent, while several alternatives are consistent.
Inference with few treated units
Authors: Luis Alvarez, Bruno Ferman, Kaspar Wüthrich
Date: 2025-04-28 00:00:00
Tasks: Causal Inference

In many causal inference applications, only one or a few units (or clusters of units) are treated. An important challenge in such settings is that standard inference methods that rely on asymptotic theory may be unreliable, even when the total number of units is large. This survey reviews and categorizes inference methods that are designed to accommodate few treated units, considering both cross-sectional and panel data methods. We discuss trade-offs and connections between different approaches. In doing so, we propose slight modifications to improve the finite-sample validity of some methods, and we also provide theoretical justifications for existing heuristic approaches that have been proposed in the literature.
Identifying the Frontier Structural Function and Bounding Mean Deviations
Authors: Dan Ben-Moshe, David Genesove
Date: 2025-04-28 00:00:00
Tasks: valid

This paper analyzes a model in which an outcome variable equals a frontier function of inputs less a nonnegative unobserved deviation. If zero lies in the support of the deviation given inputs, then the frontier function is identified by the supremum outcome at those inputs. This obviates the need for instrumental variables. Implementation requires allowing for the distribution of deviations to depend on inputs, thus not ruling out endogeneity. Including random errors yields a stochastic frontier analysis model. We generalize this model to allow the joint distribution of deviations and errors to depend on inputs. We derive a lower bound for the mean deviation based only on variance and skewness, requiring no other distributional assumptions and remaining valid even when zero is not in the support of deviations or data are sparse near the frontier. We apply our results to a frontier production function, where deviations represent inefficiencies.
Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity
Authors: Kaicheng Chen
Date: 2025-04-26 00:00:00

Panel data allows for the modeling of unobserved heterogeneity, significantly raising the number of nuisance parameters and making high dimensionality a practical issue. Meanwhile, temporal and cross-sectional dependence in panel data further complicates high-dimensional estimation and inference. This paper proposes a toolkit for high-dimensional panel models with large cross-sectional and time sample sizes. To reduce the dimensionality, I propose a weighted LASSO using two-way cluster-robust penalty weights. Although consistent, the convergence rate of LASSO is slow due to the cluster dependence, rendering inference challenging in general. Nevertheless, asymptotic normality can be established in a semiparametric moment-restriction model by leveraging a clustered-panel cross-fitting approach and, as a special case, in a partial linear model using the full sample. In a panel estimation of the government spending multiplier, I demonstrate how high dimensionality could be hidden and how the proposed toolkit enables flexible modeling and robust inference.
Regularized Generalized Covariance (RGCov) Estimator
Authors: Francesco Giancaterini, Alain Hecq, Joann Jasiak, Aryan Manafi Neyazi
Date: 2025-04-25 00:00:00

We introduce a regularized Generalized Covariance (RGCov) estimator as an extension of the GCov estimator to high dimensional setting that results either from high-dimensional data or a large number of nonlinear transformations used in the objective function. The approach relies on a ridge-type regularization for high-dimensional matrix inversion in the objective function of the GCov. The RGCov estimator is consistent and asymptotically normally distributed. We provide the conditions under which it can reach semiparametric efficiency and discuss the selection of the optimal regularization parameter. We also examine the diagonal GCov estimator, which simplifies the computation of the objective function. The GCov-based specification test, and the test for nonlinear serial dependence (NLSD) are extended to the regularized RGCov specification and RNLSD tests with asymptotic Chi-square distributions. Simulation studies show that the RGCov estimator and the regularized tests perform well in the high dimensional setting. We apply the RGCov to estimate the mixed causal and noncausal VAR model of stock prices of green energy companies.
MLOps Monitoring at Scale for Digital Platforms
Authors: Yu Jeffrey Hu, Jeroen Rombouts, Ines Wilms
Date: 2025-04-23 00:00:00

Machine learning models are widely recognized for their strong performance in forecasting. To keep that performance in streaming data settings, they have to be monitored and frequently re-trained. This can be done with machine learning operations (MLOps) techniques under supervision of an MLOps engineer. However, in digital platform settings where the number of data streams is typically large and unstable, standard monitoring becomes either suboptimal or too labor intensive for the MLOps engineer. As a consequence, companies often fall back on very simple worse performing ML models without monitoring. We solve this problem by adopting a design science approach and introducing a new monitoring framework, the Machine Learning Monitoring Agent (MLMA), that is designed to work at scale for any ML model with reasonable labor cost. A key feature of our framework concerns test-based automated re-training based on a data-adaptive reference loss batch. The MLOps engineer is kept in the loop via key metrics and also acts, pro-actively or retrospectively, to maintain performance of the ML model in the production stage. We conduct a large-scale test at a last-mile delivery platform to empirically validate our monitoring framework.
Evaluating Meta-Regression Techniques: A Simulation Study on Heterogeneity in Location and Time
Authors: Ali Habibnia, Jonathan Gendron
Date: 2025-04-23 00:00:00
Tasks: Model Selection

In this paper, we conduct a simulation study to evaluate conventional meta-regression approaches (study-level random, fixed, and mixed effects) against seven methodology specifications new to meta-regressions that control joint heterogeneity in location and time (including a new one that we introduce). We systematically vary heterogeneity levels to assess statistical power, estimator bias and model robustness for each methodology specification. This assessment focuses on three aspects: performance under joint heterogeneity in location and time, the effectiveness of our proposed settings incorporating location fixed effects and study-level fixed effects with a time trend, as well as guidelines for model selection. The results show that jointly modeling heterogeneity when heterogeneity is in both dimensions improves performance compared to modeling only one type of heterogeneity.
Dynamic Discrete-Continuous Choice Models: Identification and Conditional Choice Probability Estimation
Authors: Christophe Bruneel-Zupanc
Date: 2025-04-23 00:00:00
Tasks: quantile regression

This paper develops a general framework for dynamic models in which individuals simultaneously make both discrete and continuous choices. The framework incorporates a wide range of unobserved heterogeneity. I show that such models are nonparametrically identified. Based on constructive identification arguments, I build a novel two-step estimation method in the lineage of Hotz and Miller (1993) and Arcidiacono and Miller (2011) but extended to simultaneous discrete-continuous choice. In the first step, I recover the (type-dependent) optimal choices with an expectation-maximization algorithm and instrumental variable quantile regression. In the second step, I estimate the primitives of the model taking the estimated optimal choices as given. The method is especially attractive for complex dynamic models because it significantly reduces the computational burden associated with their estimation compared to alternative full solution methods.
Global identification of dynamic panel models with interactive effects
Authors: Jushan Bai, Pablo Mones
Date: 2025-04-19 00:00:00

This paper examines the problem of global identification in dynamic panel models with interactive effects, a fundamental issue in econometric theory. We focus on the setting where the number of cross-sectional units (N) is large, but the time dimension (T) remains fixed. While local identification based on the Jacobian matrix is well understood and relatively straightforward to establish, achieving global identification remains a significant challenge. Under a set of mild and easily satisfied conditions, we demonstrate that the parameters of the model are globally identified, ensuring that no two distinct parameter values generate the same probability distribution of the observed data. Our findings contribute to the broader literature on identification in panel data models and have important implications for empirical research that relies on interactive effects.
Finite Population Identification and Design-Based Sensitivity Analysis
Authors: Brendan Kline, Matthew A. Masten
Date: 2025-04-19 00:00:00
Tasks: Sensitivity

We develop a new approach for quantifying uncertainty in finite populations, by using design distributions to calibrate sensitivity parameters in finite population identified sets. This yields uncertainty intervals that can be interpreted as identified sets, Bayesian credible sets, or frequentist design-based confidence sets. We focus on quantifying uncertainty about the average treatment effect (ATE) due to missing potential outcomes in a randomized experiment, where our approach (1) yields design-based confidence intervals for ATE which allow for heterogeneous treatment effects but do not rely on asymptotics, (2) provides a new motivation for examining covariate balance, and (3) gives a new formal analysis of the role of randomized treatment assignment. We illustrate our approach in three empirical applications.
Projection Inference for set-identified SVARs
Authors: Bulat Gafarov, Matthias Meier, José Luis Montiel Olea
Date: 2025-04-18 00:00:00

We study the properties of projection inference for set-identified Structural Vector Autoregressions. A nominal $1-\alpha$ projection region collects the structural parameters that are compatible with a $1-\alpha$ Wald ellipsoid for the model's reduced-form parameters (autoregressive coefficients and the covariance matrix of residuals). We show that projection inference can be applied to a general class of stationary models, is computationally feasible, and -- as the sample size grows large -- it produces regions for the structural parameters and their identified set with both frequentist coverage and \emph{robust} Bayesian credibility of at least $1-\alpha$. A drawback of the projection approach is that both coverage and robust credibility may be strictly above their nominal level. Following the work of \cite{Kaido_Molinari_Stoye:2014}, we `calibrate' the radius of the Wald ellipsoid to guarantee that -- for a given posterior on the reduced-form parameters -- the robust Bayesian credibility of the projection method is exactly $1-\alpha$. If the bounds of the identified set are differentiable, our calibrated projection also covers the identified set with probability $1-\alpha$. %eliminating the excess of robust Bayesian credibility also eliminates excessive frequentist coverage. We illustrate the main results of the paper using the demand/supply-model for the U.S. labor market in Baumeister_Hamilton(2015)
Who Flees Conflict?
Authors: Lidia Ceriani, Paolo Verme
Date: 2025-05-06 00:00:00

Despite the growing numbers of forcibly displaced persons worldwide, many people living under conflict choose not to flee. Individuals face two lotteries - staying or leaving - characterized by two distributions of potential outcomes. This paper proposes to model the choice between these two lotteries using quantile maximization as opposed to expected utility theory. The paper posits that risk-averse individuals aim at minimizing losses by choosing the lottery with the best outcome at the lower end of the distribution, whereas risk-tolerant individuals aim at maximizing gains by choosing the lottery with the best outcome at the higher end of the distribution. Using a rich set of household and conflict panel data from Nigeria, the paper finds that risk-tolerant individuals have a significant preference for staying and risk-averse individuals have a significant preference for fleeing, in line with the predictions of the quantile maximization model. These findings are in contrast to findings on economic migrants, and call for separate policies toward economic and forced migrants.