Search Results for author: Sudeepa Roy

Found 9 papers, 4 papers with code

Causal Relational Learning

no code implementations7 Apr 2020 Babak Salimi, Harsh Parikh, Moe Kayali, Sudeepa Roy, Lise Getoor, Dan Suciu

Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making.

Causal Inference Decision Making +1

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.

Interpretable Almost Matching Exactly for Causal Inference

3 code implementations18 Jun 2018 Yameng Liu, Aw Dieng, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.

Causal Inference

A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence

no code implementations8 Aug 2017 Sudeepa Roy, Babak Salimi

The study of causality or causal inference - how much a given treatment causally affects a given outcome in a population - goes way beyond correlation or association analysis of variables, and is critical in making sound data driven decisions and policies in a multitude of applications.

Causal Inference

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

Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases

no code implementations26 Sep 2013 Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu

The best current methods for exactly computing the number of satisfying assignments, or the satisfying probability, of Boolean formulas can be seen, either directly or indirectly, as building 'decision-DNNF' (decision decomposable negation normal form) representations of the input Boolean formulas.

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