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.
Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making.
We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space.
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.
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.
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.
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.
In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets.
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.