Agent skill · research science · aj-geddes
Causal Inference
Determine cause-and-effect relationships using propensity scoring, instrumental variables, and causal graphs for policy evaluation and treatment effects
Why this skill is useful
Provides domain-specific knowledge on causal inference methods and implementation techniques that the AI wouldn't reliably generate on its own.
What it needs
About 4k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Causal Inference Overview Causal inference determines cause-and-effect relationships and estimates treatment effects, going beyond correlation to understand what causes what. When to Use Evaluating the impact of policy interventions or business decisions Estimating treatment effects when randomized experiments aren't feasible Controlling for confounding variables in observational data Determining if a marketing campaign or product change caused an outcome Analyzing heterogeneous treatment effects across different user segments Making causal claims from non-experimental data using propensity scores or instrumental variables Key Concepts Treatment: Intervention or exposure Outcome: Result or consequence Confounding: Variables affecting both treatment and outcome Causal Graph: Visual representation of relationships Treatment Effect: Impact of intervention Selection Bias: Non-random treatment assignment Causal Methods Randomized Controlled Trials (RCT): Gold standard Propensity Score Matching: Balance treatment/control Difference-in-Differences: Before/after comparison Instrumental Variables: Handle endogeneity Causal Forests: Heterogeneous treatment effects Implementation with Python Causal Assumptions Unconfoundedness: No unmeasured confounders Overlap: Common support on propensity scores SUTVA: No interference between units Consistency: Single version of treatment Treatment Effect Types ATE: Average Treatment Effect (overall) ATT: Average Treatment on Treated CATE: Conditional Average Treatment Effect HTE: Heterogeneous Treatment Effects Method Strengths RCT: Gold standard, controls all confounders Matching: Balances groups, preserves overlap Regression: Adjusts for covariates Instrumental Variables: Handles endogeneity Causal Forests: Learns heterogeneous effects Deliverables Causal graph visualization Treatment effect estimates Sensitivity analysis Heterogeneous treatment effects Covariate balance assessment Propensity score diagnostics Final causal inference repor …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/causal-inference