activity
20162024
most citedA causal fused lasso for interpretable heterogeneous treatment effects estimation

1 citations · 1 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2024

Sample size planning for conditional counterfactual mean estimation with a K-armed randomized experiment

Gabriel Ruiz

We cover how to determine a sufficiently large sample size for a -armed randomized experiment in order to estimate conditional counterfactual expectations in data-driven subgrou…

stat.ML2022

Neuroevolutionary Feature Representations for Causal Inference

Michael C. Burkhart, Gabriel Ruiz

Within the field of causal inference, we consider the problem of estimating heterogeneous treatment effects from data. We propose and validate a novel approach for learning feature…

stat.ME2022

High confidence inference on the probability an individual benefits from treatment using experimental or observational data with known propensity scores

Gabriel Ruiz, Oscar Hernan Madrid Padilla

We seek to understand the probability an individual benefits from treatment (PIBT), an inestimable quantity that must be bounded in practice. Given the innate uncertainty in the po…

stat.ME2022

Sequentially learning the topological ordering of causal directed acyclic graphs with likelihood ratio scores

Gabriel Ruiz, Oscar Hernan Madrid Padilla, Qing Zhou

Causal discovery, the learning of causality in a data mining scenario, has been of strong scientific and theoretical interest as a starting point to identify "what causes what?" Co…

stat.ME2021★ 1 cited

A causal fused lasso for interpretable heterogeneous treatment effects estimation

Oscar Hernan Madrid Padilla, Yanzhen Chen, Carlos Misael Madrid Padilla +1

We propose a novel method for estimating heterogeneous treatment effects based on the fused lasso. By first ordering samples based on the propensity or prognostic score, we match u…

stat.ME2018

Maximum value of the standardized log of odds ratio and celestial mechanics

Olga A. Vsevolozhskaya, Gabriel Ruiz, Dmitri V. Zaykin

The odds ratio (OR) is a widely used measure of the effect size in observational research. ORs reflect statistical association between a binary outcome, such as the presence of a h…

q-bio.GN2016

Assessment of P-value variability in the current replicability crisis

Olga A. Vsevolozhskaya, Gabriel Ruiz, Dmitri V. Zaykin

Increased availability of data and accessibility of computational tools in recent years have created unprecedented opportunities for scientific research driven by statistical analy…

q-bio.GN2016

The more you test, the more you find: Smallest P-values become increasingly enriched with real findings as more tests are conducted

Olga A. Vsevolozhskaya, Chia-Ling Kuo, Gabriel Ruiz +2

Increasing accessibility of data to researchers makes it possible to conduct massive amounts of statistical testing. Rather than follow a carefully crafted set of scientific hypoth…