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Princeton University

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Selected work

Representative Papers

Difference-in-Differences Estimators for Treatments Continuously Distributed at Every Period

Jan 18, 2022Social Science Research Network

This paper addresses causal effect estimation under continuous, time-varying treatments (e.g., taxes, tariffs, prices), extending the canonical difference-in-differences (DID) framework. Methodologically, it introduces a longitudinal comparison identification strategy anchored at baseline treatment levels to identify a weighted average of the treatment effect slope; constructs a doubly robust, √n-consistent, and asymptotically normal nonparametric estimator; and rigorously generalizes DID to settings with continuous treatment in every period—including an extension to instrumental variable settings. The approach avoids strong parametric assumptions on the treatment function and preserves testability of the parallel trends assumption. Empirically, the method successfully estimates the price elasticity of gasoline demand, demonstrating its validity and robustness in real-world economic policy evaluation.

32 citations2 influentialRead paper

Double Robustness of Local Projections and Some Unpleasant VARithmetic

May 01, 2024Social Science Research Network

This paper investigates coverage robustness of impulse response inference in locally misspecified vector autoregression (VAR) models. We find that conventional VAR confidence intervals exhibit severe undercoverage—even for statistically subtle, theoretically admissible misspecifications—when lag orders are short to moderate. In contrast, local projection (LP) confidence intervals demonstrate double robustness: they maintain nominal coverage under strong misspecification and asymptotically match or exceed VAR performance under weak misspecification. We establish, for the first time, that LP possesses a semilinear-regression–like double-robust structure and rigorously prove that VAR inference achieves asymptotic robustness only as the lag order diverges to infinity. Asymptotic expansions and Monte Carlo simulations confirm that LP consistently sustains nominal coverage across diverse misspecification regimes, whereas VAR exhibits substantially deflated coverage—and narrower, misleadingly precise intervals—under standard lag selections.

14 citationsRead paper

From Uncertainty to Trust: Enhancing Reliability in Vision-Language Models with Uncertainty-Guided Dropout Decoding

Dec 09, 2024arXiv.org

LVLMs frequently suffer from hallucinations and unreliable outputs due to misinterpretation of visual inputs. To address this, we propose an uncertainty-guided inference-time visual token dropout method: (1) the first adaptation of dropout to the visual token level during inference; (2) decoupled modeling of epistemic and aleatoric uncertainty, with explicit focus on quantifying perceptual errors; (3) uncertainty estimation via projection of visual tokens into the text embedding space, followed by weighted masking; and (4) robust, training-free correction via multi-context masked decoding and ensemble prediction. Evaluated on CHAIR, THRONE, and MMBench, our method significantly reduces object hallucination (OH) while substantially improving output reliability and cross-scenario generation quality.

13 citationsRead paper

Bridging Language and Action: A Survey of Language-Conditioned Robot Manipulation

Dec 17, 2023

This work addresses the semantic gap between natural language instructions and robotic physical actions to enhance the naturalness and reliability of human-robot collaboration. We propose the first four-dimensional taxonomy for language-conditioned robotic manipulation—comprising reward shaping, policy learning, neurosymbolic AI, and foundation model–driven approaches—and systematically analyze their fundamental limitations in generalization and safety. Integrating large language models (LLMs), vision-language models (VLMs), neurosymbolic reasoning, and multimodal semantic parsing, we develop a unified analytical framework spanning semantic extraction, environmental assessment, and auxiliary task design. Our analysis rigorously characterizes the performance boundaries of each paradigm for the first time, establishing theoretical foundations and concrete technical pathways toward safe, generalizable, and interpretable language-driven robotic systems.

10 citationsRead paper

ARctic Escape: Promoting Social Connection, Teamwork, and Collaboration Using a Co-Located Augmented Reality Escape Room

Apr 19, 2023CHI Extended Abstracts

Existing AR escape rooms predominantly support single-user experiences, failing to replicate the social collaboration inherent in physical escape rooms. To address this, we design and implement a co-located two-player AR escape room system featuring a novel “collaborative triggering” mechanism: critical virtual clues require real-time, synchronized interaction from both players—thereby mandating face-to-face communication and coordinated teamwork. Built on ARKit and ARCore, the system integrates spatial anchors, bimanual gesture recognition, and distributed state synchronization to ensure consistent shared virtual environments and low-latency interactivity across users. A user study demonstrates significantly increased interaction frequency and verbal discussion, validating the system’s effectiveness in enhancing social presence. However, some participants reported transient spatial disorientation induced by virtual content, highlighting an important direction for future refinement.

10 citationsRead paper
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Bellman Policy Optimization

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0 citationsRead paper