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causal-inference

Compute causal effects from observational data using do-calculus and DAGs.

6|Updated Apr 16, 2026
Search Tags:#dag#counterfactuals#do-calculus#causal-inference#potential-outcomes#front-door#back-door
One-click install
npx skills add https://github.com/the-thinker0/math-skill --skill causal-inference-the-thinker0
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: causal-inference
Source: https://github.com/the-thinker0/math-skill/tree/main/skills/causal-inference
Command: npx skills add https://github.com/the-thinker0/math-skill --skill causal-inference-the-thinker0

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Causal inference helps researchers and data scientists distinguish correlation from causation, enabling evaluation of interventions, policy impacts, and counterfactual reasoning.

Core Features & Use Cases

  • Identify causal relationships using DAGs and do-calculus to estimate P(y|do(x)).
  • Plan experiments and natural experiments, mediation analysis, and counterfactual reasoning to understand "what if" scenarios.
  • Apply to policy evaluation, education research, and general scientific inquiry to separate true causal effects from confounding.

Quick Start

Pose a causal question and the skill will outline identification steps and compute P(y|do(x)).

Frequently Asked Questions about causal-inference

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I estimate causal effects from observational data instead of just correlations?▼

To estimate causal effects from observational data, you must encode explicit causal assumptions in DAGs. The skill uses do-calculus and identification criteria to compute P(y|do(x)), separating true causal impacts from confounding variables.

What is the best way to calculate P(y|do(x)) for policy evaluation?▼

Calculating P(y|do(x)) for policy evaluation requires applying do-calculus to a structural causal model. The skill identifies causal relationships using DAGs and computes intervention effects to assess policy impacts accurately.

Do I need a DAG to perform counterfactual reasoning and mediation analysis?▼

Yes, counterfactual reasoning and mediation analysis require explicit causal assumptions encoded in a DAG or structural causal model. The skill uses these structures to estimate potential outcomes and analyze "what if" scenarios.

Can I use back-door and front-door criteria for intervention evaluation without running randomized experiments?▼

Yes, you can evaluate interventions without randomized experiments by applying back-door and front-door identification criteria. The skill uses these criteria with do-calculus to estimate true intervention effects from observational data.

When should I not use do-calculus for estimating causal relationships?▼

You should not use do-calculus when causal assumptions cannot be credibly encoded in a DAG. If the structural causal model is invalid or unidentifiable, the computed P(y|do(x)) and counterfactual outcomes will be unreliable.

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