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Causal Inference Meets Machine Learning

Published: 20 August 2020 Publication History

Abstract

Causal inference has numerous real-world applications in many domains such as health care, marketing, political science and online advertising. Treatment effect estimation, a fundamental problem in causal inference, has been extensively studied in statistics for decades. However, traditional treatment effect estimation methods may not well handle large-scale and high-dimensional heterogeneous data. In recent years, an emerging research direction has attracted increasing attention in the broad artificial intelligence field, which combines the advantages of traditional treatment effect estimation approaches (e.g., matching estimators) and advanced representation learning approaches (e.g., deep neural networks). In this tutorial, we will introduce both traditional and state-of-the-art representation learning algorithms for treatment effect estimation. Background about causal inference, counterfactuals and matching estimators will be covered as well. We will also showcase promising applications of these methods in different application domains.

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  • (2025)Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, EthiopiaBMC Medical Informatics and Decision Making10.1186/s12911-025-02917-925:1Online publication date: 7-Feb-2025
  • (2025)Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice YieldEngineering Reports10.1002/eng2.130857:1Online publication date: 16-Jan-2025
  • (2024)Machine Learning Algorithms for Survival Analysis: Advantages, Disadvantages, and ExamplesInternational Journal of Artificial Intelligence and Machine Learning10.51483/IJAIML.4.1.2024.10-214:1(10-21)Online publication date: 5-Jan-2024
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  1. Causal Inference Meets Machine Learning

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    cover image ACM Conferences
    KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
    August 2020
    3664 pages
    ISBN:9781450379984
    DOI:10.1145/3394486
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 20 August 2020

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    1. causal inference
    2. machine learning

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    • (2025)Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, EthiopiaBMC Medical Informatics and Decision Making10.1186/s12911-025-02917-925:1Online publication date: 7-Feb-2025
    • (2025)Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice YieldEngineering Reports10.1002/eng2.130857:1Online publication date: 16-Jan-2025
    • (2024)Machine Learning Algorithms for Survival Analysis: Advantages, Disadvantages, and ExamplesInternational Journal of Artificial Intelligence and Machine Learning10.51483/IJAIML.4.1.2024.10-214:1(10-21)Online publication date: 5-Jan-2024
    • (2024)Causal Economic Machine Learning (CEML): “Human AI”AI10.3390/ai50400945:4(1893-1917)Online publication date: 11-Oct-2024
    • (2024)A Causal Explainable Guardrails for Large Language ModelsProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3690217(1136-1150)Online publication date: 2-Dec-2024
    • (2024)Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT)10.1109/AIIoT58432.2024.10574726(1-6)Online publication date: 3-May-2024
    • (2024)Advanced Deep Learning Models for 6G: Overview, Opportunities, and ChallengesIEEE Access10.1109/ACCESS.2024.341890012(133245-133314)Online publication date: 2024
    • (2024)Inferring the heterogeneous effect of urban land use on building height with causal machine learningGIScience & Remote Sensing10.1080/15481603.2024.232169561:1Online publication date: 25-Feb-2024
    • (2024)The delayed and combinatorial response of online public opinion to the real world: An inquiry into news texts during the COVID-19 eraHumanities and Social Sciences Communications10.1057/s41599-024-03530-311:1Online publication date: 10-Aug-2024
    • (2024)Pooled lagged effect of runoff on leptospirosis cases in ColombiaHeliyon10.1016/j.heliyon.2024.e32882(e32882)Online publication date: Jun-2024
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