no code implementations • 4 Jul 2023 • Harsh Parikh, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
Experimental and observational studies often lack validity due to untestable assumptions.
no code implementations • 3 Jul 2023 • Amirhossein Farzam, Shashank Shekhar, Isaac Mehlhaff, Marco Morucci
The successful analysis of argumentative techniques from user-generated text is central to many downstream tasks such as political and market analysis.
no code implementations • 3 Apr 2023 • Marco Morucci, Cynthia Rudin, Alexander Volfovsky
We introduce Matched Machine Learning, a framework that combines the flexibility of machine learning black boxes with the interpretability of matching, a longstanding tool in observational causal inference.
1 code implementation • 6 Jan 2021 • Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates.
1 code implementation • 3 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.
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.
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.
2 code implementations • 5 Dec 2018 • Marco Morucci, Md. Noor-E-Alam, Cynthia Rudin
However, as we show in this work, there is a typical source of uncertainty that is essentially never considered in observational causal studies: the choice of match assignment for matched groups, that is, which unit is matched to which other unit before a hypothesis test is conducted.
Methodology
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.