4 citations · 13 across the 10 of their papers we have counts for
10 papers
Multi-Task Learning Improves Performance In Deep Argument Mining Models
Amirhossein Farzam, Shashank Shekhar, Isaac Mehlhaff +1
The successful analysis of argumentative techniques from user-generated text is central to many downstream tasks such as political and market analysis. Recent argument mining tools…
A Double Machine Learning Approach to Combining Experimental and Observational Data
Harsh Parikh, Marco Morucci, Vittorio Orlandi +3
Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational st…
Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics
Marco Morucci, Cynthia Rudin, Alexander Volfovsky
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 obser…
Measurement That Matches Theory: Theory-Driven Identification in IRT Models
Marco Morucci, Margaret Foster, Kaitlyn Webster +2
Measurement bridges theory and empirics. Without measures that appropriately capture theoretical concepts, description will fail to represent reality and true causal inference will…
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang +9
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. This package implements the Dynamic Almost Mat…
Matching Bounds: How Choice of Matching Algorithm Impacts Treatment Effects Estimates and What to Do about It
Marco Morucci, Cynthia Rudin
Many major works in social science employ matching to make causal conclusions, but different matches on the same data may produce different treatment effect estimates, even when th…