arXiv research

Method provides statistical guarantees for identifying subgroups in ML studies.

Study identifies and estimates treatment effect heterogeneity within principal stratification subpopulations.

Simplified proof for asset pricing theory.

New method combines strengths of two PCL approaches without density ratio estimation.

Kernel ridge regression for causal inference with missing data.

Kernel methods identify treatment effects with unobserved confounding using negative controls.

We review some recent results in the generic rigidity theory of planar frameworks with forced symmetry, giving a uniform treatment to the topic. We also give new combinatorial characterizations of minimally rigid periodic frameworks with fixed-area fundamental domain and fixed-angle fundamental domain.

Method estimates treatment effect bounds in sample selection models.

Randomized Controlled Trials (RCTs) are the gold standard for comparing the effectiveness of a new treatment to the current one (the control). Most RCTs allocate the patients to the treatment group and the control group by uniform randomization. We show that this procedure can be highly sub-optimal (in terms of learnin…

We give a uniform and elementary treatment of many classical and new triply periodic minimal surfaces in Euclidean space, based on a Schwarz-Christoffel formula for periodic polygons in the plane. Our surfaces share the property that vertical symmetry planes cut them into simply connected pieces.

Paper derives policy rules from observational data for hepatitis C treatment.

Estimates causal effects using machine learning for binary treatment and mediator.

New method estimates heterogeneous treatment effects with improved guarantees.

Proposes a method to estimate causal effects of continuous treatments using instrumental variables.

Causal trees struggle with accuracy in estimating treatment effects.

We derive parametrizations of the Delaunay constant mean curvature surfaces of revolution that follow directly from parametrizations of the conics that generate these surfaces via the corresponding roulette. This uniform treatment exploits the natural geometry of the conic (parabolic, elliptic or hyperbolic) and leads …

The paper develops methods for causal function estimation and inference with multiway clustered data.

Estimates RL data for dynamic treatment effects using GMM.

This paper tackles post-trade allocation inefficiencies and presents a uniform return allocation method.

In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects. Ad-hoc methods have been developed for each estimand based on inverse probability weighting (IPW) and on outcome regr…

Proposes estimators for complex dose-response curves using kernel methods.

Efficient inference method for adaptive experiments with tighter confidence sequences.

Develops robust and efficient SS estimators for treatment effects.

This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools. We represent this structural function as a conditional expec…

We provide adaptive inference methods, based on $\ell_1$ regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects, and derivatives. Examples of non-regular f…

This paper introduces time-uniform CLT-based confidence intervals for statistical inference.

Develops methods to estimate ratios of conditional expectation functions.

Study of surfaces in space forms using Lie sphere geometry.

Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by {dropout} and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions (which shift propensity scores rather than set tre…

Unified framework for robust clustering under various dissimilarity measures.

A new algorithm speeds up rerandomization for better experiment balance.

Wasserstein Policy Learning for Distributional Outcomes

This paper tackles fair online decision-making in contextual bandits, achieving optimal performance and fairness.

Randomization is minimax-optimal for variance in experimental design, even with structure.

The paper challenges the use of decision trees for pointwise inference due to slow convergence rates.

New kernel methods estimate complex causal relationships.

Optimal algorithm for identifying best-arm with minimal regret.

GraphITE estimates individual effects of graph-structured treatments.

M3E2 neural network estimates multiple treatment effects.

There is a significant literature on methods for incorporating knowledge into multiple testing procedures so as to improve their power and precision. Some common forms of prior knowledge include (a) beliefs about which hypotheses are null, modeled by non-uniform prior weights; (b) differing importances of hypotheses, m…

Proposes a new method to estimate continuous treatment policies and match treatments effectively.

We consider the notion of dimension in four categories: the category of (unbounded) separable metric spaces and (metrically proper) Lipschitz maps, and the category of (unbounded) separable metric spaces and (metrically proper) uniform maps. A unified treatment is given to the large scale dimension and the small scale …

This primer explains diffusion models in general state spaces.

Framework generates personalized insulin treatment strategies using deep models.

Develops deep jump learning for continuous treatment OPE.

Proves existence of solutions with concentrated energy in 2+1 spacetime.

Method controls treatment risk in learning beneficial allocations.

Optimal adaptive experiment for choosing best treatment with binary outcomes.