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Machine Learning for Causal Inference

Published onNov 28, 2022
Machine Learning for Causal Inference

Part I The Causal Inference Problems

1.1. The Philosophy of Causal Inference.

Referring to Oxford English Dictionary, etiology, a word derived from the Greek αἰτιολογία (aitiología) “giving a reason for” (αἰτία, aitía, "cause"); and -λογία (-logía) is the study of causes, origins, or reasons behind the way that things are. Causal inference is stronger than the inference of association in analyzing the response of an effect variable when the cause of the effect variable is changed.

A case study: Card, David. "The causal effect of education on earnings." Handbook of labor economics 3 (1999): 1801-1863.

Related readings:

1.2. Causal Inference in Nobel Prize of Economists.

1.3. Causal Inference in Turing Awards of Computer Scientist.

Part II Other Open Educational Resources

  • Microsoft Research at NeurIPS

    • Competition: Causal Insights for Learning Paths in Education

    • NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education

      Deep End-to-end Causal Inference

    • Rhino: Deep Causal Temporal Relationship Learning with history-dependent noise

    • Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning

    • A Causal AI Suite for Decision-Making

Part III The Algorithms and Workflow for Causal Inference

1. Regression Discontinuity

Regression Discontinuity Design and Instrumental Variables | Causal Inference in Data Science Part 4
Susan Athey: Synthetic Difference in Differences
Update on Microsoft causal open-source libraries | Community Workshop on Microsoft's Causal Tools

Source: Screenshot from

Introduction to ShowWhy, user interfaces for causal decision making
  • Alex and Causal Python


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