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Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Sensitivity Analysis in Observational Studies
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Sensitivity Analysis in Observational Studies
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Fairness-Aware Classifier with Prejudice Remover Regularizer
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An Introduction to Sensitivity Analysis for Unobserved Confounding in Nonexperimental Prevention Research
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Exposing the probabilistic causal structure of discrimination
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Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation
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Causal Inference for Statistics, Social, and Biomedical Sciences
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A multidisciplinary survey on discrimination analysis
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Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation
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Estimating causal effects of treatments in randomized and nonrandomized studies.
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Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score
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Statistics and Causal Inference
inferredcites →https://doi.org/10.1089/big.2016.0047openalex-snapshot
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
inferredcites →https://doi.org/10.1093/biomet/70.1.41openalex-snapshot
The central role of the propensity score in observational studies for causal effects
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Marginal Structural Models and Causal Inference in Epidemiology
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On the Causal Interpretation of Race in Regressions Adjusting for Confounding and Mediating Variables
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Discrimination detection by causal effect estimation
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Classifying without discriminating
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Building Classifiers with Independency Constraints
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3. The Foundations of Causal Inference
inferredcites →https://doi.org/10.1111/j.1467-985x.2007.00527.xopenalex-snapshot
8. Misunderstandings Between Experimentalists and Observationalists About Causal Inference
inferredcites →https://doi.org/10.1111/j.2517-6161.1991.tb01848.xopenalex-snapshot
A Characterization of Optimal Designs for Observational Studies
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Certifying and Removing Disparate Impact
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Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
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Fairness through Causal Awareness
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Inherent Tradeoffs in the Fair Determination of Risk Scores
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Fairness in Criminal Justice Risk Assessments: The State of the Art
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Causal Inference Using Potential Outcomes
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Matching Methods for Causal Inference: A Review and a Look Forward
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An algorithm for removing sensitive information: Application to race-independent recidivism prediction
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Bayesian Inference for Causal Effects: The Role of Randomization
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On the Interpretation of do(x)do(x)
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Fairness in Decision-Making — The Causal Explanation Formula
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Path-Specific Counterfactual Fairness
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MatchIt : Nonparametric Preprocessing for Parametric Causal Inference
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ipw: AnRPackage for Inverse Probability Weighting
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Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment
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The Central Role of the Propensity Score in Observational Studies for Causal Effects
inferredcites →https://doi.org/10.2307/2683903openalex-snapshot
Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score
inferredcites →https://doi.org/10.24963/ijcai.2017openalex-snapshot
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
inferredcites →https://doi.org/10.24963/ijcai.2017/549openalex-snapshot
A Causal Framework for Discovering and Removing Direct and Indirect Discrimination
inferredcites →https://doi.org/10.32614/cran.package.matchitopenalex-snapshot
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
inferredcites →https://doi.org/10.4230/lipics.itcs.2017.43openalex-snapshot
Inherent Trade-Offs in the Fair Determination of Risk Scores
inferredcites →https://doi.org/10.4230/lipics.itcs.2019.33openalex-snapshot
Fairness Under Composition
inferredcites →https://doi.org/10.48550/arxiv.1507.05259openalex-snapshot
Fairness Constraints: Mechanisms for Fair Classification
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A survey on measuring indirect discrimination in machine learning
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The Variational Fair Autoencoder
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Equality of Opportunity in Supervised Learning
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Fairness in Criminal Justice Risk Assessments: The State of the Art
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Avoiding Discrimination through Causal Reasoning
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Big Data, Data Science, and Civil Rights
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Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment
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Causal Reasoning for Algorithmic Fairness
inferredcites →https://doi.org/10.48550/arxiv.1806.02380openalex-snapshot
Causal Interventions for Fairness
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When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Fair Machine Learning Through the Lens of Causality
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Advancing fairness in natural language processing : from traditional methods to explainability
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