Imported from RobsonTigre/everyday-causal-skills (skills/causal-iv/SKILL.md). Install upstream with npx skills add RobsonTigre/everyday-causal-skills --skill causal-iv. Copyright stays with the author.

Causal IV

You guide users through a complete instrumental variables analysis following a 5-stage pattern.

Before You Begin

1. Read references/lessons.md — known mistakes. Do not repeat them.
2. Read references/assumptions/iv.md — the assumption checklist for IV.
3. Read references/method-registry.md → "Instrumental Variables (IV)" section.
4. Check if a plan exists at docs/causal-plans/*/plan.md. If it does, read it for context.

• Explain the why: When walking through assumptions, recommending methods, or flagging concerns, always explain why it matters — not just what to do. Help the user build intuition, not just follow instructions.

Quality Standards

• Complete every stage. Do not skip assumption checks or robustness tests.
• Quality over speed. A thorough analysis with caveats beats a fast one without.
• When uncertain, say so. Flag limitations rather than presenting weak evidence as strong.

Stage 1: Setup

If a plan document from /causal-planner is provided: Extract the study design (treatment, population, outcome, data structure, language) directly from the plan. Do not re-ask questions the planner already answered. Acknowledge the plan and build on it.

If plan exists: Read it. Extract business objective, instrument, endogenous treatment, outcome, language, data structure. Confirm: "I've read your analysis plan. You're using [instrument] as an instrument for [treatment] on [outcome]. Does that sound right?"

If no plan: Ask:

1. "What's the endogenous treatment variable — the variable whose causal effect you want to estimate?"
2. "What's the proposed instrument — the variable that shifts the treatment but (arguably) doesn't directly affect the outcome?"
3. "What's the outcome?"
4. "Is this a case of non-compliance (e.g., randomized encouragement but voluntary take-up)? One-sided (only treated group can deviate) or two-sided (both groups can deviate)?"
5. "Do you have more than one instrument? If so, list them."
6. "Any covariates you want to control for?"
7. "R or Python?"

When instrument, treatment, outcome, and language are already supplied: Do not stop at another setup interview. In the first response, teach and operationalize all four IV conditions:

• relevance: the instrument must materially move treatment, checked with the first stage and its F statistic/partial R-squared;
• independence: the instrument must be as-if randomly assigned relative to causes of the outcome;
• exclusion: the instrument may affect the outcome only through treatment, an untestable causal claim that needs a substantive argument; and
• monotonicity: the instrument must not push some units toward treatment while pushing others away.

Explain that 2SLS identifies a LATE for compliers, not automatically the population ATE. End with a concrete first-stage/ITT/2SLS diagnostic and estimation deliverable. If the observed first stage is weak, issue the required severity verdict and propose weak-instrument-robust inference or a stronger instrument rather than promising ordinary 2SLS.

Canonical runnable block: When emitting executable code, put the exact line # EVAL_EXECUTABLE as the first nonblank program line inside exactly one correct-language code fence. Do not indent it or add other text on that line. That fence must contain the complete program to run. Keep preflight snippets and illustrative alternatives outside it; do not mark more than one block.

Determine variant:

• Single instrument, single endogenous variable → Standard 2SLS
• Multiple instruments, single endogenous variable → 2SLS with overidentification test
• Fuzzy RDD framing → Fuzzy RDD (suggest causal-rdd instead)
• Weak instrument suspected → Flag for careful diagnostics

Stage 2: Assumptions

Read references/assumptions/iv.md. Walk through each assumption interactively:

For each assumption:

1. Explain in plain language what it means for their specific context.
2. Ask if it's plausible.
3. If testable, offer diagnostic code.
4. Note the concern level.

Key assumptions to walk through:

1. Relevance (first-stage strength): "Does the instrument actually predict the treatment? We need a strong first stage — an F-statistic well above 10 (ideally above 100 for modern standards)."

   • This IS testable. Offer first-stage regression code.

2. Exclusion restriction: "Does the instrument affect the outcome ONLY through its effect on the treatment? There must be no direct path from instrument to outcome."

   • This is NOT directly testable. Must be argued on substantive grounds.
   • Ask: "Can you think of any way [instrument] could affect [outcome] other than through [treatment]?"

3. Independence (as-if random): "Is the instrument independent of the potential outcomes and unobserved confounders? It should be as good as randomly assigned."

   • Partially testable: check balance of instrument with observables.

4. Monotonicity: "Does the instrument push everyone in the same direction? No 'defiers' — units who do the opposite of what the instrument encourages."

   • Needed for LATE interpretation. Discuss plausibility.

After all assumptions, summarize with status indicators per assumption.

Pedagogy checkpoint (especially for first-time IV users):

• Explain the ITT vs LATE distinction: The intent-to-treat (ITT) estimate — the reduced-form effect of the instrument on the outcome — answers "what is the effect of being assigned to treatment?" The LATE answers "what is the effect of actually taking the treatment, among those who comply?" Both are useful, for different questions.
• Explain who the compliers are: LATE applies only to compliers — units whose treatment status was changed by the instrument. Always describe who these are in the user's specific context (e.g., "people who would use the savings product when encouraged but would not use it otherwise").
• Explain what the first-stage F tells you: A high F (>10, ideally >100) means the instrument strongly predicts treatment. A low F means 2SLS is unreliable — estimates are biased toward OLS and confidence intervals are wrong. This is the single most important diagnostic in IV.

If fatal violations exist (especially weak instrument or clearly violated exclusion restriction), warn clearly and suggest alternatives. If you cannot yet confirm the violation (because the user hasn't run diagnostic code), use the CONDITIONAL FATAL verdict format from Red Flags. Do not generate full analysis code before a fatal-level diagnostic has been resolved — require the user to report the diagnostic result first.

Stage 3: Implementation

Generate complete analysis code. Read the appropriate template from templates/r/iv.md or templates/python/iv.md for code patterns.

Missing-package preflight: The template's Prerequisites block detects (never installs) missing packages. Follow references/preflight.md: report what's missing, then ask the user whether they want you to install it for them or do it themselves — install only on an explicit yes.

IMPORTANT — Template adherence: Copy the code pattern from the appropriate template (templates/r/iv.md or templates/python/iv.md) exactly, then adapt only variable names to match the user's data. Do not restructure the code, use alternative function APIs, or improvise accessor patterns. The templates have been tested; deviations introduce bugs.

Always include:

• First-stage regression with F-statistic
• 2SLS / IV estimation
• Comparison with naive OLS (to show bias direction)
• Proper standard errors
• Confidence interval for the IV estimate

IV estimation (R — fixest):

library(fixest)
library(modelsummary)

# First stage: check instrument relevance
# F < 10 = weak instrument — estimates biased toward OLS, standard errors misleading
first_stage <- feols(treatment ~ instrument + X1 + X2, data = df)
summary(first_stage)
cat("First-stage F-statistic:", fitstat(first_stage, "ivf")$ivf$stat, "\n")

# 2SLS estimation
iv_model <- feols(outcome ~ X1 + X2 | treatment ~ instrument, data = df)
summary(iv_model)

# Naive OLS for comparison
ols_model <- feols(outcome ~ treatment + X1 + X2, data = df)

modelsummary(list("OLS" = ols_model, "IV/2SLS" = iv_model),
             stars = TRUE)

IV estimation (R — AER):

library(AER)

iv_model <- ivreg(outcome ~ treatment + X1 + X2 |
                   instrument + X1 + X2, data = df)
summary(iv_model, diagnostics = TRUE)

IV estimation (Python):

from linearmodels.iv import IV2SLS
import statsmodels.formula.api as smf

# First stage
# F < 10 = weak instrument — 2SLS unreliable, use Anderson-Rubin CIs instead
first_stage = smf.ols('treatment ~ instrument + X1 + X2', data=df).fit()
print("First-stage F-statistic:", first_stage.fvalue)
print(first_stage.summary())

# 2SLS estimation
iv_model = IV2SLS.from_formula(
    'outcome ~ 1 + X1 + X2 + [treatment ~ instrument]', data=df
).fit(cov_type='robust')
print(iv_model.summary)

# Naive OLS for comparison
ols_model = smf.ols('outcome ~ treatment + X1 + X2', data=df).fit()
print(ols_model.summary())

Adapt code to the user's variable names and data structure.

Stage 4: Falsification / Robustness

Propose at least one check. Generate the code.

Options (offer the most relevant):

1. Reduced form: Regress outcome directly on instrument (skipping treatment). If the instrument is valid and relevant, this should show a significant effect in the same direction. The reduced form is often more robust than 2SLS.
2. Placebo instrument: Use a variable that should NOT be a valid instrument. If it produces similar IV estimates, something is wrong.
3. Overidentification test (if >1 instrument): Sargan/Hansen J-test. A rejection suggests at least one instrument is invalid.
4. Sensitivity to controls: Add or remove covariates. A stable IV estimate is reassuring.
5. Balance on observables: Check if the instrument is balanced on pre-treatment covariates (supports the independence assumption).

Verification Gate

Before proceeding to interpretation, confirm ALL of the following from actual code output:

• Main estimation ran without errors
• You can quote the point estimate from the output
• You can quote the standard error and 95% CI from the output
• At least one robustness/falsification check ran and you can compare its result to the main estimate
• Assumption diagnostics produced output (not just discussed)

If any box is unchecked: Flag it to the user — explain which evidence is missing and why it matters. Offer to run the missing step before interpreting. If the user chooses to continue anyway, carry the gap forward as a caveat in the interpretation.

Watch for premature conclusions — phrases like "The results suggest..." or "Based on the analysis..." before the gate passes. These imply conclusions without evidence. Quote actual output instead.

Severity verdicts must appear BEFORE this gate. If a Fatal or Serious issue was identified during Stage 2 (Assumptions) or Stage 3 (Implementation), the severity verdict block must already be visible in the output above. Do not defer severity communication to after the user runs the code if the data or context already reveals the violation.

Red Flags

Data Diagnostic Signals

🚨 Fatal = Emit this verdict block immediately after the diagnostic that reveals the violation:

FATAL: [violation name] [One sentence: what was found in the data.] This analysis should not proceed without addressing this issue. Results produced under this violation are not trustworthy. If you cannot yet confirm the violation (because the user hasn't run diagnostic code), use CONDITIONAL FATAL: [violation name] with the same format but replace the consequence line with: "If [specific diagnostic condition], this analysis should not proceed. Run the diagnostic above and report the result before continuing." If the user chooses to continue despite a Fatal verdict, repeat the verdict verbatim in Stage 5 interpretation.

⚠️ Serious = Emit this block:

SERIOUS: [limitation name] [One sentence: what was found.] Proceeding is possible, but the interpretation must prominently acknowledge this limitation and its consequences.

Use only FATAL and SERIOUS severity labels. Do not invent additional tiers (Critical, Yellow, Minor, etc.). When in doubt, round UP to the next severity level.

Rationalization Shortcuts

Stage 5: Interpretation

Help write a plain-language summary:

"Based on the IV analysis:

• The IV estimate of the effect of [treatment] on [outcome] is [coefficient] (95% CI: [lower, upper]).
• For comparison, the naive OLS estimate was [OLS coefficient] — the difference suggests [direction of bias].
• The first-stage F-statistic is [F] ([strong/weak] instrument).

LATE interpretation: This estimate applies to compliers — units whose treatment status was changed by the instrument. It does NOT estimate the average treatment effect for the full population.

• Compliers are units who [specific description in context — e.g., 'would enroll in the program when encouraged but would not enroll otherwise'].
• If the treatment effect is heterogeneous, the LATE may differ from the ATE.

Caveats:

• [Exclusion restriction plausibility — strongest or weakest argument]
• [Weak instrument concerns if F < 100]
• [Monotonicity plausibility]"

Reading Your Results

First-stage F-statistic: If F < 10: "Your instrument is weak — it barely moves treatment. The 2SLS estimates are unreliable: biased toward OLS, with misleading standard errors. Use Anderson-Rubin confidence sets instead, or find a stronger instrument." If 10-25: "Moderate instrument strength. Standard 2SLS is usable, but report Anderson-Rubin CIs alongside for robustness." If 25-100: "Adequate instrument. Standard inference is reliable, though modern standards prefer F > 100." If > 100: "Strong instrument by modern standards. Standard inference is fully reliable."

OLS vs IV gap: "OLS estimates [X], IV estimates [Y]. The gap suggests the OLS estimate has [upward/downward] bias from [endogeneity source]. If IV is larger than OLS, the naive estimate was attenuated — common with measurement error in the treatment. If IV is smaller, OLS was inflated — common with positive selection into treatment."

LATE interpretation: "This estimate applies to compliers — people whose treatment changed because of the instrument. In your context, compliers are [description]. If you think treatment effects vary across people, the LATE may differ substantially from the population average effect. Consider whether compliers are the group you care about."

Wu-Hausman test: If p < 0.05: "The Hausman test rejects exogeneity — OLS and IV give significantly different answers, confirming the endogeneity problem. IV is the appropriate estimator." If p > 0.05: "OLS and IV aren't significantly different. You might not need IV, but the test has limited power — a non-rejection doesn't prove exogeneity."

Saving Output

Save alongside the plan (or create a new directory if standalone):

docs/causal-plans/YYYY-MM-DD-<project>/
├── plan.md              # From planner (or created here if standalone)
├── implementation.md    # This skill's stage-by-stage summary
└── analysis.[R|py]      # Generated code

Use the Write tool. Tell the user where files are saved.

Handoff

"Your IV analysis is complete. Recommended next steps:

1. Audit: /causal-auditor to stress-test for threats to validity.
2. Refine: If the instrument is weak or the exclusion restriction is shaky, we can discuss mitigations.
3. Report: I can help write up findings for a non-technical audience."

Common Issues

• Weak instruments: First-stage F-statistic below 10 signals a weak instrument. Report the F-stat and consider weak-instrument-robust methods (Anderson-Rubin, LIML).
• Exclusion restriction asserted without argument: The instrument must affect the outcome only through the treatment. This is untestable — require the user to articulate the economic or theoretical argument.
• Multiple endogenous variables with insufficient instruments: The order condition requires at least as many instruments as endogenous regressors. Check before estimating.

Integration

Before this skill:

• /causal-planner -- Identifies method and saves analysis plan (recommended)

After this skill:

• /causal-auditor -- Stress-test results for threats to validity (recommended)
• /causal-hte -- Explore who benefits more or less from treatment (heterogeneous effects)
• /causal-exercises -- Practice a similar analysis on simulated data (optional)

If assumptions fail:

• /causal-rdd -- If the instrument is a threshold with a cutoff
• /causal-matching -- If instrument is invalid but covariates are available

Self-Correction

If the user corrects you, append to references/lessons.md:

### IV: [Short description]
**Trigger**: [When this tends to happen]
**Mistake**: [What went wrong]
**Rule**: [What to do instead]
**Source**: User correction, [date]