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Do-notation

As explained in Causal AI

Do-notation represents the probability distribution resulting from externally setting one or more variables to specified values.

Do-notation expresses the distribution produced by externally setting a variable, separating causal intervention from ordinary observation. P(I|E="high") describes players observed with high engagement; P(I|do(E="high")) describes purchases when engagement is set to high. section 7.3.1

An ideal intervention targets a variable, fixes its value, and removes causal influence from that variable’s parents. In a DAG this is graph surgery; in an SCM it replaces the variable’s assignment function. section 8.2.3

The notation supports several causal queries:

  • Joint distributions under intervention, such as P(I, G|do(E="high")). section 7.3.1
  • Multiple interventions, such as setting engagement to high and guild membership to nonmember. section 7.3.1
  • Average treatment effects, computed as E(Y|do(X=1)) − E(Y|do(X=0)) for binary causes. section 7.4.1

Engagement and in-game purchases

Asking what purchases would be for players who happen to have high engagement uses conditional probability. Asking what purchases would result if engagement were made high uses do-notation, excluding the non-causal association through guild membership. section 8.2.3

Table 7.11 Examples of do-notation and counterfactual notation
Literal
Do-notation
Counterfactual notation
The probability distribution of In-Game Purchases across all players
P( I  )
P( I  )
The probability distribution of In-Game Purchases for players with “high” engagement
P( I  | E=“high”)
P( I  | E=“high”)
The probability distribution of In-Game Purchases when a player’s engagement level is set (by intervention) to “high”
P( I  |do( E= “high”))
P( I   E = “high”)
The joint probability distribution of In-Game Purchases and Guild Membership when engagement is set to “high”
P( I, G|do( E=“high”))
P( I   E=“high”, G E=“high”)
The probability distribution of In-Game Purchases when engagement is set to “high” and membership is set to “nonmember”
P( I  |do( E  =“high”, G=“nonmember”))
P( I   E = “high”, G =“nonmember”)
The probability distribution of In-Game Purchases for guild members when engagement is set to “high”
P( I  |do( E  =“high”), G  =  “member”)
P( I E   = “high”| G  =  “member”)

Compare observational, interventional, joint, and multiple-intervention expressions with their counterfactual equivalents. section 7.3.1

How Causal AI covers it

How deep it goes
Defines notation, extends it to joint and multiple interventions, and connects it to DAG surgery, SCMs, and causal libraries.
Examples
Uses engagement, in-game purchases, guild membership, binary treatments, and A/B-test-style treatment and control values.
What it assumes
Assumes probability distributions and introduces the distinction between conditioning, intervention, and counterfactual notation.
What you can do after
Evaluate/understand causal effects by translating hypothetical interventions into distributions and average treatment-effect expressions.
How it connects
Connects conditional probability, counterfactuals, average treatment effects, directed acyclic graphs, and structural causal models.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Causal AI. The section links point to the parts of the book it draws on.