A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New methods estimate heterogeneous causal effects at various levels.
Estimates causal effects using machine learning for binary treatment and mediator.
The paper uses double machine learning to estimate dynamic treatment effects robustly.
The paper develops methods to estimate treatment effects in sample selection models.
In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application-specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited se…
This chapter introduces flexible methods for policy evaluation.
New methods for estimating causal effects with limited overlap, using Stable Probability Weighting.