Triage Score: A Counterfactual Risk Assessment Instrument (SIMONS-ECON)
39:56LLM-Guided Reinforcement Learning for Mastery Learning: Large-Scale Field Evidence (SIMONS-ECON)
35:06Non-parametric Causal Inference in Dynamic Thresholding Designs (SIMONS-ECON)
30:21Agreement and Alignment for Human-AI Collaborative Decision Making (SIMONS-ECON)
45:21Human Values in Clinical Algorithms, Old and New (SIMONS-ECON)
38:46When Prediction Becomes Policy: What Do Risk Models Actually Learn? (SIMONS-ECON)
33:31Evaluating Probabilistic Decision Making by LLMs: A Decision-Theoretic Framework and Application (SIMONS-ECON)
32:16Algorithmic UDAP (SIMONS-ECON)
32:13Operational Dosage: The Impact of Capacity Constraints on RCT Evaluation (SIMONS-ECON)
35:01The Role of Standardized Tests in Admissions: Predictions vs. Decisions (SIMONS-ECON)
33:37Individual Gaps in Prediction Generalization (SIMONS-ECON)
30:41Beyond Prediction Performance: How Modeling Decisions Shape Fairness Outcomes in Statistical Profiling (SIMONS-ECON)
27:56The Feedback Loop of Statistical Discrimination (SIMONS-ECON)
32:03Decision Theoretic Foundations for Human-AI Collaboration (SIMONS-ECON)
31:01Evaluation Design (SIMONS-ECON)
29:16Recommendation in Equilibrium for the NYC High School Match (SIMONS-ECON)
32:51Automating the US Welfare State: Historical Experiments in Technological Federalism (SIMONS-ECON)
35:41On Machine Learning for Prediction and Prioritization in the Allocation of Scarce Societal Resources (SIMONS-ECON)
35:26Non-parametric Causal Inference in Dynamic Thresholding Designs (SIMONS-ECON)
This talk examines non-parametric causal inference in dynamic thresholding designs, addressing the limitations of naive regression-discontinuity analysis when temporal dynamics are present.
Stefan Wager (Stanford University)
Bridging Prediction and Intervention Problems in Social Systems
Consider a setting where we regularly monitor patients' fasting blood sugar, and declare them to have prediabetes (and encourage preventative care) if this number crosses a pre-specified threshold. The sharp, threshold-based treatment policy suggests that we should be able to estimate the long-term benefit of this preventative care by comparing the health trajectories of patients with blood sugar measurements right above and below the threshold. A naive regression-discontinuity analysis, however, is not applicable here, as it ignores the temporal dynamics of the problem where, e.g., a patient just below the threshold on one visit may become prediabetic (and receive treatment) following their next visit. Here, we study thresholding designs in general dynamic systems, and show that simple reduced-form characterizations remain available for a relevant causal target, namely a dynamic marginal policy effect at the treatment threshold. We develop a local-linear-regression approach for estimation and inference of this estimand, and demonstrate promise of our approach in numerical experiments. More broadly, we will highlight the promise of policy-gradient methods for causal inference in observational studies.
LLM-Guided Reinforcement Learning for Mastery Learning: Large-Scale Field Evidence (SIMONS-ECON)
Next · Lecture 4Agreement and Alignment for Human-AI Collaborative Decision Making (SIMONS-ECON)