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Identify_effect

As explained in Causal AI

identify_effect is a DoWhy method that derives candidate causal estimands from a causal model and its observed variables.

Estimating the effect of Side-Quest Engagement on In-Game Purchases requires more than a predictive relationship: identify_effect derives candidate causal estimands from the DAG, observed variables, and causal query. section 11.3

After a CausalModel is constructed from a pandas DataFrame, treatment, outcome, and graph, model.identify_effect() returns an IdentifiedEstimand containing available estimands and their assumptions. section 11.3

The method can expose several identification strategies:

  • Backdoor adjustment using Player Skill Level, Guild Membership, and Time Spent Playing. section 11.3
  • Front-door adjustment using Won Items as a mediator. section 11.3
  • Instrumental-variable estimands using Side-Quest Group Assignment and Customization Level. section 11.3

Run identification

identified_estimand = model.identify_effect()\nprint(identified_estimand) section 11.3

Listing 11.4 Run identification in DoWhy
identified_estimand = model.identify_effect()    #1
print(identified_estimand)

Use the listing to inspect the direct DoWhy call that performs identification after model construction. section 11.3

How Causal AI covers it

How deep it goes
Enumerates estimands and their assumptions, but stops before estimating effects or validating every causal assumption.
Examples
Demonstrates game data with Side-Quest Engagement, In-Game Purchases, latent Prior Experience, and three candidate estimands.
What it assumes
Requires a causal query, DAG, observed variables, and explicit assumptions about confounding and latent variables.
What you can do after
Apply DoWhy's CausalModel API to list candidate estimands and compare their required assumptions.
How it connects
Connects causal models, DAGs, causal queries, confounding, backdoor adjustment, front-door adjustment, and instrumental variables.
Cover of Causal AI
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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.