Causal Discovery

5 Steps

1. 1

   Introduction to Causal Discovery: Understand the fundamental concepts of causal discovery, including causal relationships, directed acyclic graphs (DAGs), and the difference between correlation and causation.

2. 2

   Constraint-Based Algorithms (PC Algorithm): Explore constraint-based algorithms like the PC algorithm. Learn how it uses conditional independence tests to identify potential causal relationships and construct a DAG.

3. 3

   Score-Based Algorithms (GES): Learn about score-based algorithms like GES (Greedy Equivalence Search). Understand how they use a scoring function (e.g., BIC, MDL) to evaluate different DAG structures and search for the one that best fits the data.

4. 4

   Functional Causal Models (FCMs): Explore Functional Causal Models (FCMs) and their application in causal discovery. Understand how they model causal relationships as functions and can handle non-linear relationships.

5. 5

   Evaluating and Interpreting Results: Learn how to evaluate the results of causal discovery algorithms, including assessing the plausibility of the discovered causal relationships and considering potential biases and limitations.