Causal Inference for Business Decision-Making

Module 1: causal questions and DAGs

Establish the vocabulary, boundaries, stakeholders, and decision context for causal questions and DAGs. Use architecture choices, evidence-based evaluation, and an applied solution review to test assumptions against the client context.

Separate established requirements and reliable evidence from untested assumptions about causal questions and DAGs. Record the decision, supporting evidence, and unresolved questions in the solution canvas.

Map causal questions and DAGs to the organization’s current responsibilities, dependencies, and constraints. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 2: experiments and quasi-experiments

Diagnose the current state of experiments and quasi-experiments using a structured evaluation lab. Use architecture choices, evidence-based evaluation, and an applied solution review to test assumptions against the client context.

Compare alternative methods and select an approach suited to risk, maturity, and scale. Record the decision, supporting evidence, and unresolved questions in the evaluation lab.

Document requirements, owners, decision criteria, and exceptions for experiments and quasi-experiments. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 3: confounding and bias

Design the workflow, safeguards, and handoffs required for confounding and bias. Use architecture choices, evidence-based evaluation, and an applied solution review to test assumptions against the client context.

Test normal, failure, and edge-case scenarios before operational adoption. Record the decision, supporting evidence, and unresolved questions in the production-readiness review.

Review the design for security, quality, accessibility, sustainability, or assurance implications as relevant. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 4: effect estimation and communication

Define meaningful measures, evidence, review cadence, and escalation thresholds for effect estimation and communication. Use architecture choices, evidence-based evaluation, and an applied solution review to test assumptions against the client context.

Investigate performance gaps and separate root causes from symptoms. Record the decision, supporting evidence, and unresolved questions in the solution canvas.

Plan corrective action, controlled change, and accountable follow-through. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 5: Applied Causal Inference for Business Decision-Making Workshop

Complete a evaluation lab that integrates the course decisions around causal analysis workshop. Use architecture choices, evidence-based evaluation, and an applied solution review to test assumptions against the client context.

Defend recommendations against a realistic stakeholder challenge or scenario. Record the decision, supporting evidence, and unresolved questions in the evaluation lab.

Produce a prioritized workplace action plan with owners, dependencies, and review points. State what would trigger rejection, escalation, or redesign of the proposed approach.