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University of California, Santa Barbara

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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

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Jan 17, 2026

This work addresses the challenge that existing AI agent benchmarks inadequately evaluate performance on real-world, complex, and long-horizon command-line tasks. To bridge this gap, the authors introduce a novel evaluation benchmark comprising 89 high-difficulty terminal tasks, all derived from authentic workflows and accompanied by isolated execution environments, human-authored reference solutions, and automated verification tests. The benchmark is designed to ensure realism, verifiability, and diversity, substantially narrowing the disparity between practical scenarios and current model evaluation paradigms. Experimental results demonstrate that even state-of-the-art agents achieve success rates below 65% on this benchmark. The paper further provides comprehensive error analysis and publicly releases the dataset and evaluation toolchain to support future research in this domain.

9 citations1 influentialRead paper

Path Contraction Faster Than 2n

Jul 08, 2019International Colloquium on Automata, Languages and Programming

This paper addresses the Path Contraction problem: given a graph $G$, determine whether it can be transformed into a path graph $P_k$ of length $k$ via a sequence of edge contractions. A classical graph modification problem, it has long lacked exact algorithms with runtime better than $2^n$. We present the first exact algorithm with runtime $O^*(c^n)$ for some $c < 2$, thereby strictly breaking the exponential base-2 barrier. Our approach introduces the notion of *contraction-sensitive vertices*, integrates structured branching with pruning, graph induction, and dynamic state compression, and employs refined recursive analysis. The theoretical correctness is fully established. The algorithm is deterministic and achieves provable exponential speedup on $n$-vertex graphs. Moreover, our framework yields a generalizable analytical paradigm for broader graph contraction problems.

6 citationsRead paper

Bayesian Data Sketching for Varying Coefficient Regression Models

May 30, 2025

Bayesian inference for varying-coefficient regression models with large-scale functional data is computationally prohibitive due to the high cost of Markov chain Monte Carlo (MCMC). To address this, we propose a data sketching method based on randomized linear transformations that compresses both the response vector and the predictor matrix into low-dimensional representations, while preserving the full Bayesian modeling framework. Standard MCMC or variational inference tools can then be directly applied to the sketched data. This work marks the first application of Bayesian data sketching to varying-coefficient models—requiring no new model specification, custom algorithm development, or specialized hardware. Theoretically and empirically, the method maintains near-identical statistical efficacy while achieving speedups of several orders of magnitude. Moreover, it seamlessly integrates with existing Bayesian inference ecosystems.

4 citations1 influentialRead paper

Winners with Confidence: Discrete Argmin Inference with an Application to Model Selection

Aug 04, 2024

This paper addresses the problem of reliably identifying the index with the minimal mean from noisy observations—a task central to model selection, policy comparison, and discrete maximum likelihood estimation. To handle high-dimensional settings, frequent ties, and globally dependent data, we propose a test statistic grounded in asymptotic normality. Methodologically, we innovatively integrate cross-validation with differential privacy mechanisms to establish a central limit theorem applicable to non-independent data, and design an adaptive hyperparameter tuning strategy that balances bias and variance. Theoretically, our approach guarantees statistical consistency. Empirically, it significantly improves selection stability and confidence in both synthetic and real-world experiments. Overall, this work provides a new framework for discrete parameter inference under noise—one that unifies theoretical rigor with practical robustness.

4 citations1 influentialRead paper
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