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Change Log:

• 2023-11-28: The data mentioned in the talk requires full specification. It may not likely work with text or image dataset. What is more relevant to text and images is called “causal representation learning.”

Overview

Causality Ladder (Judea Pearl): Seeing \rightarrow Intervening \rightarrow Imagining
– Seeing: This is where the traditional ML happens.
– Invervening
– Imaging: This requires structural causal model (SCM). This is not discussed in the talk.

Assumptions

• Ingredients

  Besides, we need to assume (1) we have magically measured all factors; there are no confounders, and (2) iid.

  • Data: Assumes to be faithful to the graph.
  • Causal Graph: Assumes to satisfy Markov condition.

Identifying Causality

• Intuition (Janzing 2012)

  If X causes Y, then the noise pattern from X is Y is simpler than the other way around.

• Operationalizing the Intuition
  • Kolmogorov Complexity: The shortest program (in any programming language) that computes a PDF. Then if X \rightarrow Y, then K(P(X)) + K(P(Y\vert X)) \leq K(P(Y)) + K(P(X\vert Y)).
  • The formula above could be realized in practice with some assumptions in systems called SLOOPY, HECI (Xu et al. 2022 and Marx & Vreeken 2017, 2019) based on relatively simple regressions.
• These systems could be evaluated using radar plot of some established datasets.