4 citations · 13 across the 6 of their papers we have counts for
6 papers
Sparse and Faithful Explanations Without Sparse Models
Yiyang Sun, Zhi Chen, Vittorio Orlandi +2
Even if a model is not globally sparse, it is possible for decisions made from that model to be accurately and faithfully described by a small number of features. For instance, an…
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…
Density Regression with Bayesian Additive Regression Trees
Vittorio Orlandi, Jared Murray, Antonio Linero +1
Flexibly modeling how an entire density changes with covariates is an important but challenging generalization of mean and quantile regression. While existing methods for density r…
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…
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
M. Usaid Awan, Marco Morucci, Vittorio Orlandi +3
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 pot…
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
Marco Morucci, Vittorio Orlandi, Sudeepa Roy +2
We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space. These regions are large enough…