Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference

no code implementations • 2 Mar 2020 • M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others' outcomes.

Interpretable Almost-Matching-Exactly With Instrumental Variables

1 code implementation • 27 Jun 2019 • M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

Uncertainty in the estimation of the causal effect in observational studies is often due to unmeasured confounding, i. e., the presence of unobserved covariates linking treatments and outcomes.

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

no code implementations • 19 Jul 2017 • Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets.

Causal Inference Management