Agent skill · vasilyu1983
foundations-causal-inference
Causal-inference primitives: DAGs, IV, RDD, DiD, synthetic control, propensity, CATE, interference. Use when attributing confounded impact or rollout and LLM-eval confounding.
What it needs
About 11k tokens when loaded.
What this skill does
Causal Inference Foundations 12 applied causal inference primitives for impact attribution and experiment design, backed by a formal theory map. Each primitive solves a specific identification or estimation problem. Primitives are domain-agnostic: the same instrumental-variable logic that handles omitted-variable bias in econometrics handles it in product analytics; the same difference-in-differences framework that evaluates policy interventions evaluates feature rollouts. When to Apply Apply causal-inference when: "Did the change cause the outcome, or just correlate?" question A/B test is impossible (rollout already happened, ethics, ramping risk) — observational methods needed Confounding suspected — non-random treatment assignment Heterogeneous treatment effects matter (CATE, uplift) Mediation question — "is the effect through path X or path Y?" Units interfere — marketplace, social graph, shared inventory, ranking model, or agents sharing a backend resource; randomization alone does not identify the launch effect LLM evaluation pipeline uses logged data — prompt distribution, judge bias, or user self-selection confound the quality signal (Pearl's Ladder applies: estimating P(Y do(prompt)) is different from P(Y prompt)) Skip and use simpler alternatives when: Clean RCT / A/B test is already running and units do not interfere — read the result, don't re-derive it observationally. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills vasilyu1983/foundations-causal-inference