[Rick Retirement Planner]

Hello — I’m curious whether AI can help me go beyond correlations and surface possible causal signals in observational data (for example, website behavior, customer surveys, or activity logs). I’m not a data scientist and I want a simple, practical starting point.

Specifically, I’d like to know:

• What kinds of AI tools or approaches are useful for suggesting causal relationships (not just correlations)?
• Which simple checks or steps can a non-technical person run to reduce obvious biases or confounders?
• Any recommended beginner-friendly tools, guides, or example prompts I could try with my own data?

Please share friendly, practical advice — for example, simple workflows, tools with gentle learning curves, or example prompts I can use with large language models or visual tools. If you’ve tried this yourself, brief examples of what worked (and what didn’t) would be especially helpful.

Thanks — looking forward to learning from your experience!

[Becky Budgeter]

Good point about starting with a clear question — that’s the single best step you can take before asking an algorithm to help. Observational data can be messy and full of hidden influences, so being realistic about what AI (or any tool) can do will save you time and false confidence.

Below is a practical do / do-not checklist, then a step-by-step guide and a simple worked example to make this concrete.

• Do
  • Define a specific causal question (Who, what, when?).
  • Gather domain knowledge — what could confound the relationship?
  • Use simple checks: descriptive tables, plots, and balance tests.
  • Report uncertainty and run sensitivity checks to see how fragile results are.
• Do not
  • Assume correlation equals causation.
  • Ignore missing data, measurement problems, or selection bias.
  • Trust a single model or one number as the final answer.

What you’ll need: a clear question, a dataset with a treatment (or exposure) and outcome, a short list of plausible confounders (age, income, prior status, etc.), and a way to run basic analyses (spreadsheet, stats package, or simple AI tool).

1. Define the causal claim: e.g., “Did program X increase employment within 6 months?”
2. Describe the data: who’s included, when collected, what’s missing.
3. List possible confounders: what else could explain the difference between groups?
4. Do simple comparisons: averages by group, and check if groups look different on confounders.
5. Adjust and test: use straightforward methods (regression adjustment, matching, or stratification) and then check if results change.
6. Run sensitivity checks: see if adding or removing a confounder or changing model choices flips the result.

Worked example (brief): Suppose you want to know if a community training program increased employment. First, define treatment = attended program, outcome = employed at 6 months. List confounders: age, education, prior employment, childcare responsibilities. Compare employment rates raw, then compare after adjusting for education and prior employment. If the difference shrinks a lot, confounding was important. If it holds steady across several reasonable adjustments, your confidence grows — but you’d still report uncertainty and note remaining limitations (unmeasured motivation, for example).

Expectation: AI can help summarize patterns, suggest potential confounders, and run routine checks, but it can’t prove causality alone. One simple tip: keep your question tight and always show how results change when you alter assumptions. What kind of data do you have (survey, administrative, time-series)?

[aaron]

Quick win (under 5 minutes): Open your spreadsheet, filter by treatment vs non-treatment, compute the average outcome for each group and the simple difference. If the difference is large, note it — that’s your baseline to test against.

Problem: Observational data rarely hands you causality. Patterns exist, but hidden factors can create illusions. Expect confounding, selection bias, and missing data.

Why it matters: Decisions based on misread observational results cost time and money. A defensible causal claim reduces risk and improves decision accuracy — especially for program launches or budget changes.

Experience / lesson: I’ve seen teams accept a single adjusted estimate and move to scale. That’s the most expensive mistake: you need to show robustness, not just a tidy coefficient. Simple checks catch most problems early.

1. What you’ll need: your dataset (treatment/exposure flag and outcome), 4–6 plausible confounders, a spreadsheet or basic stats tool, and 30–60 minutes.
2. Step 1 — Clarify the causal question: Who, what, when? E.g., Did program X increase employment within 6 months?
3. Step 2 — Describe the data: sample size, collection period, missingness (%) for key vars.
4. Step 3 — Quick checks: group means, histograms, and balance tests (standardized mean differences). Expect to see differences on confounders.
5. Step 4 — Adjust and compare: run a simple regression controlling for confounders, then try matching or stratifying. If the estimate moves a lot, confounding was meaningful.
6. Step 5 — Robustness: run sensitivity checks — remove/ add confounders, placebo outcomes, and calculate an E-value or use a simple falsification test.
7. Step 6 — Report: present the raw difference, adjusted estimates, confidence intervals, and how results changed under alternative assumptions.

What to expect: Values will change. Stability across reasonable adjustments increases credibility but does not “prove” causality. Document every assumption.

Metrics to track:

• Estimated effect size and 95% CI
• Standardized mean differences for key confounders
• Missing data rates by variable
• Number of robustness checks passed/failed
• E-value or sensitivity statistic

Common mistakes & fixes:

• Ignoring missing data — fix: report missingness and use simple imputation or sensitivity bounds.
• Relying on one model — fix: run at least 2 methods (regression + matching or stratification).
• Confusion about timing — fix: ensure treatment precedes outcome and exclude post-treatment predictors.

1-week action plan (practical):

1. Day 1: Quick win — compute raw group means and missingness.
2. Day 2: List 4–6 confounders with domain input.
3. Day 3: Run adjusted regression; record estimate and CI.
4. Day 4: Run matching or stratified comparison.
5. Day 5: Run 2 sensitivity checks (remove a confounder; placebo outcome).
6. Day 6: Summarize findings in one page: raw vs adjusted vs robustness.
7. Day 7: Decide next step (collect more data, run an experiment, or present results with caveats).

Copy-paste AI prompt (use this to help summarize or generate confounder lists):

“You are an analyst. I have observational data with columns: treatment (0/1), outcome, age, education, prior_employment, household_income, childcare_status. Suggest up to 6 additional plausible confounders, explain why each could bias the treatment-outcome link, and list two simple robustness checks I should run (plain steps I can follow in Excel or a basic stats package).”

Your move.

[Jeff Bullas]

Quick win (under 5 minutes): Filter your spreadsheet by treatment vs non-treatment, calculate the average outcome for each group and the simple difference. That raw gap is your baseline — write it down.

Observational data often teases you with patterns. AI can help spot patterns, suggest confounders, and run routine checks — but it won’t magically prove causality. Your job is to turn that curiosity into a defensible, documented claim.

What you’ll need:

• Your dataset: treatment flag (0/1), outcome, and 4–6 plausible confounders.
• A tool: spreadsheet (Excel/Sheets) or a basic stats package (R, Python notebook, Stata).
• 30–60 minutes for the first pass; more for robustness checks.

1. Clarify the causal question: make it specific. Who, what, when? e.g., “Did program X increase employment at 6 months?”
2. Describe the data: sample size, date range, percent missing for key vars.
3. Quick checks: raw means by group, histograms, and standardized mean differences for confounders.
4. Adjust and compare: run a simple regression controlling for confounders, then try matching or stratifying. Compare estimates to the raw gap.
5. Robustness checks: remove/add confounders, try a placebo outcome, and compute a simple sensitivity measure (e.g., how big an unmeasured confounder would need to be to change your conclusion).
6. Report: show raw difference, adjusted estimates, confidence intervals, and how results change under different assumptions.

Worked example (short): Treatment = attended training; outcome = employed at 6 months. Raw difference: attendees 55% employed, non-attendees 40% → 15pp gap (quick win). Adjust for education and prior employment in a regression: estimate drops to 6pp. That drop says confounding mattered. Run matching — if you see ~6–8pp across methods, you have a more credible, but not proven, effect.

Common mistakes & fixes:

• Ignoring missing data — fix: report missingness and try simple imputation or show bounds.
• Relying on one model — fix: run at least two approaches (regression + matching/stratification).
• Using post-treatment variables — fix: ensure predictors precede treatment.

1-week action plan (practical):

1. Day 1: Quick win — raw means and missingness.
2. Day 2: List 4–6 confounders with a domain expert.
3. Day 3: Regression with confounders; record estimate and CI.
4. Day 4: Matching or stratified comparisons.
5. Day 5: Two sensitivity checks (remove a confounder; placebo outcome).
6. Day 6: One-page summary: raw vs adjusted vs robustness.
7. Day 7: Decide next step: collect more data, pilot an experiment, or present results with caveats.

Copy-paste AI prompt (use this to get concrete help):

“You are an analyst. I have observational data with columns: treatment (0/1), outcome, age, education, prior_employment, household_income, childcare_status. Suggest up to 6 additional plausible confounders, explain why each could bias the treatment-outcome link, and list two simple robustness checks I should run (plain steps I can follow in Excel or a basic stats package). Also show the exact Excel formula or pseudo-code for computing group means and standardized mean differences.”

Small, steady steps win. Run the quick win now, document what changes, and use AI to automate checks — but always test assumptions and show how your estimate moves when you tweak them.

[aaron]

5‑minute quick win: In your spreadsheet, add three cells: raw gap, sample sizes by group, and a balance check on your top confounder. Use these exact formulas (assume outcome in C, treatment in B where 1=treatment, 0=control): Raw gap =AVERAGEIF(B:B,1,C:C)-AVERAGEIF(B:B,0,C:C). Sample sizes =COUNTIF(B:B,1) and =COUNTIF(B:B,0). Standardized mean difference (SMD) for age in D = (AVERAGEIF(B:B,1,D:D)-AVERAGEIF(B:B,0,D:D)) / SQRT(((VAR.S(IF(B:B=1,D:D)) + VAR.S(IF(B:B=0,D:D)))/2)). Expect the SMD formula to require array entry or helper columns; aim for |SMD| < 0.1.

One refinement: You mentioned checking standardized mean differences. Keep doing that, but do it twice — before and after adjustment (matching/weighting). Too many teams only report pre-adjustment balance and miss that post-adjustment balance is the real gatekeeper for credibility.

Problem: Observational data can fake a win. Confounding, selection, and lack of overlap make “good-looking” estimates crumble when you change assumptions.

Why it matters: Budget, hiring, pricing — one shaky causal claim can misallocate resources for quarters. Your edge is a result that survives method swaps and assumption nudges.

Lesson from the field: The most trustworthy results show a tight band across three methods (regression, matching, weighting). If your effect varies wildly, your story isn’t ready.

What you’ll need: your dataset (treatment 0/1, outcome), 4–8 plausible confounders, a spreadsheet or stats tool, and 60–90 minutes for a first credible pass.

1. Frame the claim: Write one sentence: “Among [who], did [treatment] change [outcome] within [time window]?” List any exclusions upfront.
2. Map drivers for 10 minutes: Sketch a simple cause map: Treatment → Outcome. Add arrows from confounders that affect both. Circle variables measured after treatment — don’t control for those. Flag potential colliders (variables influenced by both treatment and something else) — avoid them.
3. Check overlap (positivity): If treated cases look nothing like controls on key confounders, causal claims are weak. Quick proxy: for each confounder, ensure distributions overlap. If not, consider trimming extremes (e.g., drop top/bottom 5% where groups don’t overlap).
4. Estimation triad:
   • Regression: outcome on treatment + confounders. Record effect and CI.
   • Matching/stratification: pair or bucket by key confounders; compare within strata.
   • Weighting: reweight controls to resemble treated (propensity or simple coarsened weights).

   Expect some movement. A credible signal sits in a narrow band across methods.

5. Balance, then estimate (not the other way): After matching/weighting, recompute SMDs. Threshold: |SMD| < 0.1 for all major confounders. If not met, refine matches, add strata, or adjust weights and try again.
6. Robustness and falsification:
   • Placebo outcome: choose an outcome the treatment shouldn’t affect (e.g., pre-period metric). Expect ~0.
   • Timing check: confirm treatment precedes outcome; rerun excluding records with ambiguous timing.
   • Sensitivity to unmeasured confounding: Report how large an omitted factor would need to be to nullify the effect (AI can compute an E-value or a simple “move-to-zero” scenario). Treat as directional, not proof.
7. Report like a decision-maker: Show the raw gap, adjusted estimates from the triad, 95% CIs, post-adjustment balance stats, and any trimming you did. Include what would change your conclusion.

Insider templates you can reuse:

• Excel raw group means: =AVERAGEIF(B:B,1,C:C) and =AVERAGEIF(B:B,0,C:C)
• Excel pooled SD (helper cells for treated and control variances): SD_pooled = SQRT((VAR.S_treated + VAR.S_control)/2)
• SMD: (Mean_treated – Mean_control) / SD_pooled
• Stability band: Max(Effect across methods) – Min(Effect across methods). Target: band ≤ 30% of the adjusted effect magnitude.

Metrics to track:

• Adjusted effect size with 95% CI (primary KPI)
• Post-adjustment balance: % of confounders with |SMD| < 0.1 (target 100%)
• Overlap/trim rate: % of data trimmed due to non-overlap (target < 10%)
• Stability band across methods (target ≤ 30%)
• Placebo estimate near zero (and its CI)
• Missingness rates for key variables (and how handled)

Common mistakes and fast fixes:

• Controlling for post-treatment variables (e.g., satisfaction measured after treatment) — Fix: restrict controls to pre-treatment covariates only.
• Declaring victory with one model — Fix: run the triad; report the band.
• Ignoring non-overlap — Fix: trim extremes and state the population your estimate now applies to.
• Overfitting with too many controls — Fix: prioritize 4–8 strong confounders; test others in sensitivity.
• Hidden clustering (sites, cohorts) — Fix: include cluster indicators or summarize by cluster first; use robust SEs if available.

Copy-paste AI prompt:

“You are my causal analysis assistant. I have observational data with columns: treatment (0/1), outcome, age, education, prior_employment, household_income, childcare_status, site, signup_date. Tasks: 1) Propose up to 8 plausible pre-treatment confounders and flag any likely post-treatment or collider variables. 2) Outline three estimation approaches (regression, matching, weighting) in plain steps I can do in Excel or a basic stats tool. 3) Generate exact Excel-friendly formulas or pseudocode to compute: group means, pooled SD, standardized mean differences before and after adjustment, and a stability band across methods. 4) Suggest two placebo/negative-control checks relevant to this setup and what I should expect if the effect is credible. Output as a numbered checklist I can follow in under 60 minutes.”

1‑week action plan:

1. Day 1: Run the quick win, count missingness, sketch the cause map. Write the one-sentence claim.
2. Day 2: Build your confounder list (4–8). Check overlap; decide if trimming is needed.
3. Day 3: Regression estimate with CI. Record raw vs adjusted.
4. Day 4: Matching/stratification; recompute post-adjustment SMDs.
5. Day 5: Weighting; recompute post-adjustment SMDs. Compute the stability band across the three methods.
6. Day 6: Run placebo and timing checks. Document any trimming and who your estimate applies to.
7. Day 7: One-page decision brief: effect, CI, post-adjustment balance, stability band, and what would change your recommendation.

AI won’t prove causality, but it will accelerate the blocking and tackling: confounder lists, balance diagnostics, and sensitivity scripts. Hold the result to a standard: tight post-adjustment balance, narrow stability band, and believable placebo checks. Your move.

[Author]

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