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06/27/2023
Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost
State-of-the-art supervised NLP models achieve high accuracy but are als...
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05/29/2023
Rethinking Counterfactual Data Augmentation Under Confounding
Counterfactual data augmentation has recently emerged as a method to mit...
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05/26/2023
Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers
To address the problem of NLP classifiers learning spurious correlations...
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04/28/2023
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
The causal capabilities of large language models (LLMs) is a matter of s...
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10/22/2022
Counterfactual Generation Under Confounding
A machine learning model, under the influence of observed or unobserved ...
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10/07/2022
Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems
Given a user's input text, text-matching recommender systems output rele...
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08/17/2022
The Counterfactual-Shapley Value: Attributing Change in System Metrics
Given an unexpected change in the output metric of a large-scale system,...
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07/08/2022
Probing Classifiers are Unreliable for Concept Removal and Detection
Neural network models trained on text data have been found to encode und...
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06/15/2022
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization
Real-world data collected from multiple domains can have multiple, disti...
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02/08/2022
Machine Explanations and Human Understanding
Explanations are hypothesized to improve human understanding of machine ...
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12/26/2021
Omitted Variable Bias in Machine Learned Causal Models
We derive general, yet simple, sharp bounds on the size of the omitted v...
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11/24/2021
Causal Regularization Using Domain Priors
Neural networks leverage both causal and correlation-based relationships...
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10/28/2021
Sayer: Using Implicit Feedback to Optimize System Policies
We observe that many system policies that make threshold decisions invol...
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10/07/2021
The Connection between Out-of-Distribution Generalization and Privacy of ML Models
With the goal of generalizing to out-of-distribution (OOD) data, recent ...
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12/21/2020
The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective
Training datasets for machine learning often have some form of missingne...
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11/11/2020
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions
For many kinds of interventions, such as a new advertisement, marketing ...
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11/10/2020
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End
To explain a machine learning model, there are two main approaches: feat...
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11/09/2020
DoWhy: An End-to-End Library for Causal Inference
In addition to efficient statistical estimators of a treatment's effect,...
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09/17/2020
Counterfactual Generation and Fairness Evaluation Using Adversarially Learned Inference
Recent studies have reported biases in machine learning image classifier...
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06/12/2020
Domain Generalization using Causal Matching
Learning invariant representations has been proposed as a key technique ...
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04/21/2020
Bursts of Activity: Temporal Patterns of Help-Seeking and Support in Online Mental Health Forums
Recent years have seen a rise in social media platforms that provide pee...
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04/21/2020
Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi
The primary obstacle to developing technologies for low-resource languag...
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12/06/2019
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Explaining the output of a complex machine learning (ML) model often req...
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09/27/2019
Alleviating Privacy Attacks via Causal Learning
Machine learning models, especially deep neural networks have been shown...
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09/03/2019
Quantifying Infra-Marginality and Its Trade-off with Group Fairness
In critical decision-making scenarios, optimizing accuracy can lead to a...
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07/10/2019
Quantifying Error in the Presence of Confounders for Causal Inference
Estimating average causal effect (ACE) is useful whenever we want to kno...
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05/19/2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Post-hoc explanations of machine learning models are crucial for people ...
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02/05/2019
Learning to Prescribe Interventions for Tuberculosis Patients using Digital Adherence Data
Digital Adherence Technologies (DATs) are an increasingly popular method...
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12/04/2018
Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables
Can instrumental variables be found from data? While instrumental variab...
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11/28/2016
Split-door criterion for causal identification: Automatic search for natural experiments
Unobserved or unknown confounders complicate even the simplest attempts ...
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01/23/2014
Algorithms for Generating Ordered Solutions for Explicit AND/OR Structures
We present algorithms for generating alternative solutions for explicit ...
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09/19/2012