everyday-causal-skills

Compiles a causal analysis into a structured report with tables, figures, and method summaries. Use when user says "write a report", "summarize my analysis", "create a report", "publication-ready", "write up the results", "executive summary", "causal report", or "document my analysis". Not for running the analysis itself (use method skills) or stress-testing validity (use /causal-auditor).

Translates an estimated causal effect into financial value — incremental ROI, breakeven, projection waterfall, and a ship/kill/size verdict. Use when the user already has an effect estimate (from project artifacts or supplied directly) and asks to turn it into money, e.g. "is the investment worth it given this lift", "translate my experiment's effect into money for the CFO", "what's the incremental ROI of this estimate", "breakeven for this effect", "build the business case from my DiD result". Requires an existing causal/incremental effect estimate. Not for estimating the effect itself (use method skills), choosing a method (use /causal-planner), ordinary accounting/financial ROI with no causal estimate, or stock/real-estate returns.

Stress-tests any causal analysis for threats to validity across 5 categories identification, statistical, data quality, interpretation, and external validity. Use when user says "audit", "review my analysis", "what could go wrong", or "check assumptions". Not for implementing fixes.

Guides DAG construction and causal identification through structured conversation. Generates dagitty (R) or DoWhy (Python) code for adjustment sets, testable implications, and visualization. Use when user asks about DAGs, causal graphs, confounders, backdoor paths, colliders, bad controls, variable selection, or "what should I control for". Not for estimating causal effects (hand off to method skills).

Implements difference-in-differences in R or Python with parallel trends testing, robustness checks, and plain-language interpretation. Use when user asks about DiD, staggered rollout, TWFE, event study, or parallel trends. Not for simple pre/post without a control group.

Generates practice exercises with simulated data and known ground truth across all causal inference methods. Use when user says "practice", "exercise", "simulate", "learn causal inference", or "test my skills". Not for real data analysis.

Designs and analyzes randomized experiments with power analysis, balance checks, and robust standard errors in R or Python. Use when user asks about RCT, A/B test, power analysis, randomization, or experimental design. Not for observational data.

Estimates heterogeneous treatment effects using Causal Forest and DML with validation (BLP/GATES/CLAN/TOC) and policy learning (policytree). Use when user asks about CATE, who benefits, subgroup effects, personalization, targeting, treatment effect heterogeneity, or causal forest.

Implements instrumental variables and 2SLS in R or Python with first-stage diagnostics, weak instrument detection, and overidentification tests. Use when user mentions IV, instrument, 2SLS, non-compliance, or endogeneity. Not for cases without a plausible instrument.

Implements matching, propensity scores, IPW, and doubly-robust estimators in R or Python with balance diagnostics and sensitivity analysis. Use when user mentions matching, propensity score, observational study, confounders, selection bias, or covariate balance. Not for settings with unobserved confounding.

Implements sharp and fuzzy regression discontinuity designs in R or Python with bandwidth selection, manipulation testing, and sensitivity analysis. Use when user mentions RDD, cutoff, threshold, running variable, or discontinuity. Not for arbitrary subgroup comparisons.

Builds synthetic control counterfactuals in R or Python with donor weighting, pre-treatment fit diagnostics, and placebo tests. Use when user mentions synthetic control, single treated unit, comparative case study, or donor pool. Not for settings with many treated units.

Implements interrupted time series and CausalImpact in R or Python with pre-period fit checks, stationarity testing, and placebo validation. Use when user mentions ITS, CausalImpact, time series intervention, or pre/post with no control group. Not for panel data with multiple units.

Structured interview that identifies causal problems and recommends the right inference method with a step-by-step analysis plan. Use when user says "what method should I use", "measure impact", "causal analysis", "treatment effect", "observational data", or "does X cause Y". Not for implementing a specific method.