Search Results for author: Alexander Volfovsky

Found 17 papers, 7 papers with code

Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization

no code implementations EMNLP (CINLP) 2021 Graham Tierney, Alexander Volfovsky

We highlight the importance of using domain knowledge to perform dimension reduction on the text data, and describe a procedure to characterize indirect effects via text when the text is only observed in one arm of the experiment.

Dimensionality Reduction

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data

1 code implementation17 Dec 2023 Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors.

Causal Inference Decision Making

Estimating Trustworthy and Safe Optimal Treatment Regimes

no code implementations23 Oct 2023 Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky

Our work operationalizes a safe and interpretable framework to identify optimal treatment regimes.

Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics

no code implementations3 Apr 2023 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 observational causal inference.

Causal Inference

Variable Importance Matching for Causal Inference

1 code implementation23 Feb 2023 Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page

Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, accurate for treatment effect estimation, and scalable to high-dimensional data.

Causal Inference Feature Importance

Neighborhood Adaptive Estimators for Causal Inference under Network Interference

no code implementations7 Dec 2022 Alexandre Belloni, Fei Fang, Alexander Volfovsky

In contrast to previous work, the proposed procedure aims to approximate the relevant network interference patterns.

Causal Inference Feature Engineering

Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients

no code implementations9 Mar 2022 Harsh Parikh, Kentaro Hoffman, Haoqi Sun, Wendong Ge, Jin Jing, Rajesh Amerineni, Lin Liu, Jimeng Sun, Sahar Zafar, Aaron Struck, Alexander Volfovsky, Cynthia Rudin, M. Brandon Westover

Having a maximum EA burden greater than 75% when untreated had a 22% increased chance of a poor outcome (severe disability or death), and mild but long-lasting EA increased the risk of a poor outcome by 14%.

Causal Inference Decision Making

Author Clustering and Topic Estimation for Short Texts

1 code implementation15 Jun 2021 Graham Tierney, Christopher Bail, Alexander Volfovsky

Analysis of short text, such as social media posts, is extremely difficult because of their inherent brevity.

Clustering Uncertainty Quantification

Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

1 code implementation3 Mar 2020 Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space.

Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference

no code implementations2 Mar 2020 M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

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.

Interpretable Almost-Matching-Exactly With Instrumental Variables

1 code implementation27 Jun 2019 M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

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.

MALTS: Matching After Learning to Stretch

no code implementations18 Nov 2018 Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

In this work, we learn an interpretable distance metric for matching, which leads to substantially higher quality matches.

Causal Inference

Interpretable Almost Matching Exactly for Causal Inference

3 code implementations18 Jun 2018 Yameng Liu, Aw Dieng, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

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.

Causal Inference

Analyzing statistical and computational tradeoffs of estimation procedures

no code implementations25 Jun 2015 Daniel L. Sussman, Alexander Volfovsky, Edoardo M. Airoldi

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.

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