COURSE 16Advanced9 IN-DEPTH LESSONS

Probabilistic Models and Causality Lite

Distinguishing correlation from causation: causal Directed Acyclic Graphs (DAGs), confounding, d-separation, interventions, and identification strategies.

Start Lesson 1: Causality and correlation →← All 29 Courses

Course Syllabus & Units

Unit 1

Overview

1 lessons
1.1L2

Causality and correlation

**Correlation** means two things move together. **Causation** means intervening on one changes the other. UCLA’s DAG intro puts it sharply: “Are coffee drinkers more likely to have pancreatic cancer?” is not the same question as “If I st...

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Unit 2

Graphs

2 lessons
2.1L2

Graphical models overview

**Probabilistic graphical models** make dependence structure explicit—Bayes nets, Markov random fields, and related factorizations. **Causal** graphs are a related but stricter commitment: arrows mean assumed cause, not only allowed stat...

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2.2L2

Causal graphs literacy

**Directed acyclic graphs (DAGs)** for causal inference: nodes are variables; arrows are assumed direct causal influence. UCLA’s workshop teaches DAG basics, elemental confounding structures, and the **backdoor criterion**. Pearl’s tradi...

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Unit 3

Experiments

1 lessons
3.1L2

A/B tests as interventions

Randomized experiments that **intervene** on what users see—so differences in outcomes can support causal claims (with caveats). UCLA’s DAG materials call well-designed experiments / RCTs a gold standard because randomization balances co...

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Unit 4

Confounding

1 lessons
4.1L2

Confounding examples

**Confounders** are factors that drive both “treatment” and “outcome,” faking causation. UCLA’s coffee story: coffee appeared linked to pancreatic cancer until smoking—the shared cause—was accounted for.

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Unit 5

Schools

1 lessons
5.1L2

Pearl DAGs vs potential outcomes

Two major teaching traditions for causal inference: 1. **Potential outcomes / Rubin causal model** — each unit has potential outcomes under different treatments; experiments and careful observational designs estimate effects like ATEs. C...

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Unit 6

Identification

1 lessons
6.1L2

Identification and confounding

**Identification** asks: *given assumptions*, can the causal effect we care about be written in terms of observable distributions? **Estimation** asks how to compute that from finite data. DoWhy’s docs insist on separating those steps. C...

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Unit 7

Toolkits

1 lessons
7.1L2

DoWhy, EconML, and PyWhy

**PyWhy** is an open-source ecosystem for causal machine learning (interop libraries + shared APIs). Flagship tools called out on pywhy.org include: - **DoWhy** — end-to-end causal inference with explicit assumptions, identification, est...

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Unit 8

Ml Causal

1 lessons
8.1L2

Machine learning and causal inference

**Prediction** answers “what is likely?” **Causal inference** answers “what if we change something?” Stanford MS&E 226 teaches those as separate pillars (with inference as a third). Stanford GSB **MGTECON 634 — Machine Learning and Causa...

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