Search Results for author:
Found 9 papers, 4 papers with code
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference
In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets.
A robust approach to quantifying uncertainty in matching problems of causal inference
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
Interpretable Almost-Matching-Exactly With Instrumental Variables
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.
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
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.
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space.
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates.
Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics
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.
Multi-Task Learning Improves Performance In Deep Argument Mining Models
The successful analysis of argumentative techniques from user-generated text is central to many downstream tasks such as political and market analysis.
A Double Machine Learning Approach to Combining Experimental and Observational Data
Experimental and observational studies often lack validity due to untestable assumptions.
