# A First Course in Causal Inference

arXiv: https://arxiv.org/abs/2305.18793 · HTML: https://mlsanity.com/paper/2305.18793/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2023.

## Built on by (most cited first, 11 of 11)

- [Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs](https://mlsanity.com/paper/2402.08733/) (2024)
- [End-To-End Causal Effect Estimation from Unstructured Natural Language Data](https://mlsanity.com/paper/2407.07018/) (2024)
- [Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population](https://mlsanity.com/paper/2401.14512/) (2024)
- [An AI-powered Bayesian generative modeling approach for causal inference in observational studies](https://mlsanity.com/paper/2501.00755/) (2025)
- [Variance reduction combining pre-experiment and in-experiment data](https://mlsanity.com/paper/2410.09027/) (2024)
- [Interrogating LLM design under a fair learning doctrine](https://mlsanity.com/paper/2502.16290/) (2025)
- [Observationally Informed Adaptive Causal Experimental Design](https://mlsanity.com/paper/2603.03785/) (2026)
- [Benign overfitting beyond prediction: The ordinary least squares interpolator](https://mlsanity.com/paper/2309.15769/) (2023)
- [Reconciling Predictive Multiplicity in Practice](https://mlsanity.com/paper/2501.16549/) (2025)
- [Body Discovery of Embodied AI](https://mlsanity.com/paper/2503.19941/) (2025)
- [Ensuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPED](https://mlsanity.com/paper/2606.18750/) (2026)

## 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.