Analysis of short text, such as social media posts, is extremely difficult because it relies on observing many document-level word co-occurrence pairs.
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.
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.
In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets.
The recent explosion in the amount and dimensionality of data has exacerbated the need of trading off computational and statistical efficiency carefully, so that inference is both tractable and meaningful.