6 papers · 1 filter
CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
Jiyuan Tan, Vasilis Syrgkanis
Automating theoretical research requires generating candidate results and evaluating them reliably. Models keep getting better at the first, while the second remains hard. A common…
Bucketing the Good Apples: A Method for Diagnosing and Improving Causal Abstraction
Li Puyin, Jiyuan Tan, Ahmad Jabbar +2
We present a method for diagnosing interpretation in neural networks by identifying an input subspace where a proposed interpretation is highly faithful. Our method is particularly…
Partial Identification of Policy-Relevant Treatment Effects with Instrumental Variables via Optimal Transport
Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis
Policy-Relevant Treatment Effects (PRTEs) are generally not point-identified under standard Instrumental Variable (IV) assumptions when the instrument generates limited support in…
Adaptive Estimation and Inference in Conditional Moment Models via the Discrepancy Principle
Jiyuan Tan, Vasilis Syrgkanis
We study adaptive estimation and inference in linear inverse problems defined by conditional moment restrictions. These problems are typically ill-posed, so estimators require regu…
Mirror Descent on Riemannian Manifolds
Jiaxin Jiang, Lei Shi, Jiyuan Tan
Mirror Descent (MD) is a scalable first-order method widely used in large-scale optimization, with applications in image processing, policy optimization, and neural network trainin…
CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
Ayush Sawarni, Jiyuan Tan, Vasilis Syrgkanis
Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effect (ATE). This approach conflate…