Causality Research Internship @ LexisNexis

Welcome! This blog is dedicated to documenting Mara Hubelbank's software development and machine learning research internship with the HPCC Systems group at LexisNexis in Summer 2021.

Thursday, June 10, 2021

05.04

Week 5, Day 4

  • Forked the Causality repo; was previously only working with a local clone.
  • Read through the NetworkX library documentation, now understanding the cGraph generation code.
  • Re-read Pearl's Chapter 4 in Causal Inference in Statistics; wrote up a design plan for the CDE/NDE/NIE code.
  • Continued watching Brady Neal's Causal Inference lecture series.

    at June 10, 2021
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    Bibliography

    • HPCC Systems ML Library
    • Causality Project Description
    • Causal Inference in Statistics: A Primer (Pearl et al.)
    • Statistics and Causal Inference: A Review (Pearl)
    • Counterfactual Theories of Causation (Stanford Encyclopedia of Philosophy)
    • Causality and Counterfactuals in the Situation Calculus (Hopkins and Pearl)
    • Estimating Causal Effects (Maldonado and Greenland)
    • Causal Inference Based on Counterfactuals (Höfler)
    • Identifiability of Path-Specific Effects (Pearl et al.)
    • A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects (Malinsky et al.)
    • Direct and Indirect Effects (Pearl)
    • Lecture Series: Causal Inference

    Blog Archive

    • ▼  2021 (16)
      • ▼  June 2021 (16)
        • 07.05
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        • 05.05
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        • First Post: Midterm!
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