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.

Tuesday, June 22, 2021

07.02

Week 7, Day 2

  • Finished closest worlds implementation, using cosine similarity to compare each record (row) in the dataset against the given unit observation. 
  • Decided to filter on the counterfactual early on, to ensure that the number of closest worlds found is not reduced at the end.
  • Updated the variable data types to be consistent with the existing codebase, and added PyDoc.

at June 22, 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
      • 07.04
      • 07.03
      • 07.02
      • 07.01
      • 06.05
      • 06.04
      • 06.03
      • 06.02
      • 06.01
      • 05.05
      • 05.04
      • 05.03
      • 05.02
      • 05.01
      • First Post: Midterm!
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