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