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02/07/2023
Concept Algebra for Text-Controlled Vision Models
This paper concerns the control of text-guided generative models, where ...
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12/16/2022
Efficient Conditionally Invariant Representation Learning
We introduce the Conditional Independence Regression CovariancE (CIRCE),...
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09/30/2022
Causal Estimation for Text Data with (Apparent) Overlap Violations
Consider the problem of estimating the causal effect of some attribute o...
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08/15/2022
A Unified Causal View of Domain Invariant Representation Learning
Machine learning methods can be unreliable when deployed in domains that...
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07/04/2022
Invariant and Transportable Representations for Anti-Causal Domain Shifts
Real-world classification problems must contend with domain shift, the (...
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05/17/2022
Using Embeddings for Causal Estimation of Peer Influence in Social Networks
We address the problem of using observational data to estimate peer cont...
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09/02/2021
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
A fundamental goal of scientific research is to learn about causal relat...
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05/31/2021
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests
Informally, a `spurious correlation' is the dependence of a model on som...
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11/24/2020
Invariant Representation Learning for Treatment Effect Estimation
The defining challenge for causal inference from observational data is t...
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11/06/2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning
ML models often exhibit unexpectedly poor behavior when they are deploye...
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10/24/2020
Causal Effects of Linguistic Properties
We consider the problem of estimating the causal effects of linguistic p...
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06/19/2020
Valid Causal Inference with (Some) Invalid Instruments
Instrumental variable methods provide a powerful approach to estimating ...
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03/03/2020
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
It is a truth universally acknowledged that an observed association with...
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06/05/2019
Adapting Neural Networks for the Estimation of Treatment Effects
This paper addresses the use of neural networks for the estimation of tr...
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05/29/2019
Using Text Embeddings for Causal Inference
We address causal inference with text documents. For example, does addin...
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02/11/2019
Using Embeddings to Correct for Unobserved Confounding
We consider causal inference in the presence of unobserved confounding. ...
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11/01/2018
The Holdout Randomization Test: Principled and Easy Black Box Feature Selection
We consider the problem of feature selection using black box predictive ...
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06/27/2018
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
Empirical risk minimization is the principal tool for prediction problem...
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04/16/2018
Compressibility and Generalization in Large-Scale Deep Learning
Modern neural networks are highly overparameterized, with capacity to su...
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12/06/2017
Exchangeable modelling of relational data: checking sparsity, train-test splitting, and sparse exchangeable Poisson matrix factorization
A variety of machine learning tasks---e.g., matrix factorization, topic ...
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12/05/2017