Search Results for author: Marco Morucci

Found 6 papers, 4 papers with code

Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

1 code implementation3 Mar 2020 Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space.

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

no code implementations2 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 implementation27 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.

Hypothesis Tests That Are Robust to Choice of Matching Method

1 code implementation5 Dec 2018 Marco Morucci, Md. Noor-E-Alam, Cynthia Rudin

The quality of matched data is usually evaluated according to some metric, such as balance; however the same level of match quality can be achieved by different matches on the same data.

Methodology

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

no code implementations19 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

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