mlsanityData to 7 Oct 2026

A First Course in Causal Inference

arXiv 2305.18793 ↗First posted 30 May 2023stat.ME stat.APcited by 11

Not on the map. Why?

Abstract (arXiv)

I developed the lecture notes based on my ``Causal Inference'' course at the University of California Berkeley over the past seven years. Since half of the students were undergraduates, my lecture notes only required basic knowledge of probability theory, statistical inference, and linear and logistic regressions.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2023.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 4; 2026: 2.

11 papers.

  1. 2024Experts Don't Cheat: Learning What You Don't Know By Predicting Pairscited by 18
  2. 2024End-To-End Causal Effect Estimation from Unstructured Natural Language Datacited by 18
  3. 2025An AI-powered Bayesian generative modeling approach for causal inference in observational studiescited by 6
  4. 2024Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Populationcited by 5
  5. 2024Variance reduction combining pre-experiment and in-experiment datacited by 2
  6. 2025Interrogating LLM design under a fair learning doctrinecited by 1
  7. 2026Observationally Informed Adaptive Causal Experimental Designcited by 1
  8. 2023Benign overfitting beyond prediction: The ordinary least squares interpolatorcited by 0
  9. 2025Reconciling Predictive Multiplicity in Practicecited by 0
  10. 2025Body Discovery of Embodied AIcited by 0
  11. 2026Ensuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPEDcited by 0