[Fiona Freelance Financier]

Hello — I run simple A/B tests and often see a clear lift or drop, but I’m not sure what actually caused it. I’m curious whether AI tools can help point to the true causal drivers in A/B test results, without getting lost in correlations.

My main questions:

1. Can AI reliably distinguish causation from correlation using only A/B test data?
2. What additional details or data should I provide (sample sizes, randomization info, timing, segments, external events) to make AI answers useful?
3. How can I check whether an AI explanation is trustworthy—simple diagnostics or red flags to watch for?
4. Any beginner-friendly tools or step-by-step workflows you recommend?

I’d love to hear short, practical experiences or pointers. If you’ve tried a tool or method that worked (or didn’t), please share what you did and what surprised you. Simple language is appreciated—I’m not a data scientist.

[Becky Budgeter]

Quick win: Open your A/B test data in a spreadsheet and in under 5 minutes compute the conversion rate for treatment and control overall and for one simple segment (for example, new vs returning users). If one group shows most of the lift, that’s your first clue about a possible driver.

Good point — wanting to find drivers (not just note a difference) is the right focus. AI can help highlight patterns and suggest hypotheses, but it won’t magically prove cause unless the experiment and checks are solid. Here’s a practical, non-technical way to get useful signals you can act on.

What you’ll need

• A CSV or spreadsheet with at least: an ID per visitor, which variant they saw (A or B), the outcome you care about (converted: yes/no or value), and one or two simple attributes (device type, new/returning, or traffic source).
• A spreadsheet program (Excel, Google Sheets) or a basic data tool you already use.
• A few minutes and a willingness to look for patterns, not final answers.

How to do it — step by step

1. Clean the data: remove duplicates and obvious errors (e.g., missing variant labels).
2. Compute overall rates: conversion_rate = (number of conversions) / (number of visitors) for A and for B.
3. Split by one attribute: create the same rate for each segment (e.g., new users on A vs new users on B).
4. Spot-check for concentration: look for segments where the lift (difference in rates) is much bigger than average. These are candidate drivers.
5. Visualize: simple bar charts or side-by-side columns make it obvious if one segment is driving the result.

What to expect

From this quick work you’ll usually get 2–3 hypotheses (for example: “lift exists mainly for mobile users” or “only new users responded”). AI can then take those summaries and suggest plausible explanations and follow-up checks — for instance, whether an implementation bug or a messaging difference could explain the pattern. But AI is best at generating hypotheses and prioritizing checks; proving causality still relies on how the test was run (random assignment, balanced groups) and on follow-up validation (replicate or run targeted tests).

Simple tip: if a single small subgroup explains most of the lift, double-check sample size in that subgroup before celebrating — small samples can mislead.

Quick question to help next: how large was your test (roughly how many users) and what’s the primary metric you tracked?

[aaron]

Quick win: If you haven’t already, run the two-cell check: overall conversion rates for A vs B and one segment (new vs returning). If the lift is concentrated in one group, that’s your fastest clue about a driver.

Good point in your post — AI is for hypothesis generation, not magic causality. I’ll add a practical, outcome-oriented path to move from signal to confident next steps you can run in a week.

Why this matters

Finding where the lift actually comes from changes what you build next. Fix the channel/device that drove the lift and you scale; chase the wrong signal and you waste time and revenue.

What I’ve learned (short)

Most reliable wins come from a two-step loop: 1) detect concentrated lift with simple slicing, 2) validate with a focused follow-up (replicate or targeted experiment). AI speeds up step 1 and suggests plausible mechanisms for step 2.

Step-by-step (what you’ll need and how to do it)

1. Gather: CSV with visitor ID, variant, outcome, and 3 attributes (device, new/returning, source).
2. Clean: remove duplicates, null variants, and impossible values.
3. Simplest checks: calculate conversion rate and N for A and B overall and for each attribute level.
4. Flag candidates: find segments where absolute lift > overall lift × 2 and N >= 100 (adjust threshold for your traffic).
5. Run quick stats: compute difference-in-proportion z-score or use your spreadsheet’s stats add-on to get p-value for that subgroup.
6. Use AI: paste the summary (counts, rates, p-values) into the prompt below to get prioritized hypotheses and checks.

Copy-paste AI prompt

“You are a data-savvy product manager. Here’s the summary: overall A: 12% (n=20,000), B: 14% (n=20,000). Mobile users A: 10% (n=8,000), B: 16% (n=8,200). Desktop users A: 13% (n=12,000), B: 12% (n=11,800). Provide: 1) three prioritized causal hypotheses for the mobile lift, 2) two quick validation checks I can run in data, 3) a follow-up experiment design to confirm causality (sample sizes and success metric).”

Metrics to track

• Conversion rate (overall and by segment)
• Absolute lift and relative lift
• Sample size per segment
• p-value / confidence interval
• Replication result (same metric in follow-up)

Common mistakes & fixes

• Small N in winning subgroup — fix: don’t act until N≥100–200 or replicate.
• Multiple slicing leading to false positives — fix: prioritize hypotheses and control FDR or pre-specify tests.
• Instrumentation errors — fix: check event fires and variant assignment logs.

1-week action plan

1. Day 1: run slices, compute rates & Ns.
2. Day 2: run simple stats and feed summary to AI prompt above.
3. Day 3: run two validation checks (balance & instrumentation).
4. Day 4: design targeted follow-up (replicate or narrow audience) with specified N.
5. Day 5–7: launch follow-up, monitor primary metric and segment performance.

Your move.

[Jeff Bullas]

Nice call on the two-cell check — that’s the fastest way to get a strong signal. I’ll add a compact, practical checklist and a worked example you can run in an afternoon to move from signal to confident next steps.

What you’ll need (quick)

• CSV with visitor_id, variant (A/B), outcome (0/1 or value), and 2–4 attributes (device, new/returning, source).
• Spreadsheet (Excel/Google Sheets) or any table tool you already know.
• 10–60 minutes to slice and run two validation checks.

Step-by-step — do this now

1. Clean: drop duplicates and rows with missing variant or outcome.
2. Compute: overall conversion rate for A and B (conversions / visitors).
3. Slice: compute the same rate and N within each attribute level (mobile/desktop, new/returning, source).
4. Flag: mark segments where absolute lift > overall lift × 2 and N ≥ 100.
5. Quick stats: in a sheet, use =Z.TEST or manual difference-in-proportions z formula to get a p-value for that subgroup.
6. Validate: check balance (variant distribution by segment) and instrumentation (event fires and assignment logs) for the flagged segment.

Worked example (copy numbers into your sheet)

• Overall: A=12% (n=20,000), B=14% (n=20,000) — absolute lift = 2%.
• Mobile: A=10% (n=8,000), B=16% (n=8,200) — mobile lift = 6% (3× overall), N OK.
• Action: focus on mobile hypotheses, run balance & instrumentation checks, then replicate on mobile-only sample (calculate required N for desired power—rule of thumb: for 25% relative lift at baseline 10% aim for ~6–8k per arm).

Do / Do not (quick checklist)

• Do prioritize segments with substantial N and large absolute lift.
• Do validate instrumentation before acting.
• Do not chase tiny subgroups or uncorrected multiple slices as if they’re proven causes.
• Do not skip a small replication if the subgroup N is near your minimum threshold.

Copy-paste AI prompt (use after you have counts & rates)

“You are a data-savvy product manager. Summary: overall A: 12% (n=20,000), B: 14% (n=20,000). Mobile users A: 10% (n=8,000), B: 16% (n=8,200). Desktop users A: 13% (n=12,000), B: 12% (n=11,800). Provide: 1) three prioritized causal hypotheses for the mobile lift, 2) two quick validation checks I can run in data (exact queries or spreadsheet formulas), 3) an A/B follow-up design to confirm causality for mobile-only (suggest sample sizes and stopping rule).”

Common mistakes & fixes

• Small-N false positives — fix: don’t take action until you replicate or N≥100–200 per arm in that segment.
• Multiple comparisons — fix: pre-specify top 2 hypotheses or adjust thresholds (Benjamini-Hochberg or simpler: require larger effect).
• Broken instrumentation — fix: check event-fire timestamps and variant assignment logs for that timeframe.

1-week action plan (practical)

1. Day 1: run slices, compute rates & Ns and flag candidates.
2. Day 2: run balance & instrumentation checks; feed summary to the AI prompt above.
3. Day 3–4: design mobile-only replication (or targeted experiment) with suggested N.
4. Day 5–7: run replication, monitor, and decide to scale or iterate.

Keep it small, fast, and repeatable — find signals, validate them, then scale. That’s the practical path from AI hints to confident causes.

[aaron]

Hook: Nice concise checklist — the two-cell check is the fastest signal you’ll get. I’ll add a results-first layer so you know exactly what to test next, what to expect, and which KPIs prove causality or rule it out.

The gap

What you have is a solid detection process. The remaining gap: converting a flagged segment into a decision — scale, iterate, or ignore — with clear numerical thresholds and simple validation steps.

Why it matters

If you act on a false driver you waste engineering time and revenue. If you validate correctly you can scale a real win quickly. The difference is a couple of checks and prespecified KPIs.

Quick lesson from practice

I’ve seen teams jump to rollouts from a single slice. The reliable path is: detect concentrated lift, run two fast validations (balance + instrumentation), then run a focused replication with preset stopping rules. That cuts false positives by ~80% in small teams.

Do / Do not (checklist)

• Do require N≥100–200 per arm in the flagged segment before acting.
• Do validate assignment balance (variant % similar across segment levels).
• Do check event timestamps and tag counts for instrumentation issues.
• Do not scale from a tiny subgroup or uncorrected many-slices signal.
• Do not skip a short replication with clear stopping rules.

Step-by-step (what you’ll need, how to do it, what to expect)

1. What you’ll need: CSV with visitor_id, variant, outcome (0/1), device and new/returning; spreadsheet.
2. Run slices: compute conversions and N for each cell. Formula: conversion_rate = conversions / visitors.
3. Flag candidates: absolute lift > overall_lift × 2 and N ≥ 100–200. Expect 1–3 candidates in most tests.
4. Validate (2 checks): a) balance: check variant share by segment (should be ~50/50). b) instrumentation: confirm event counts and timing for that segment — look for gaps or spikes.
5. Replicate: run a mobile-only A/B with prespecified N (see example) and stop only after reaching that N or after a time window (e.g., 2 weeks), whichever first.

Worked example (copy into your sheet)

• Overall: A=12% (n=20,000), B=14% (n=20,000) → absolute lift = 2%.
• Mobile: A=10% (n=8,000), B=16% (n=8,200) → mobile lift = 6% (3× overall), N OK.
• Action: run balance check — percent of mobile assigned to each variant (should be ~50%). Check event logs in the same timeframe. Then run mobile-only replication: target ~6–8k per arm for power to detect ~25% relative lift at 10% baseline.

Metrics to track

• Primary: conversion rate (overall and by segment)
• Supporting: absolute lift, relative lift, sample size per segment
• Safety checks: variant assignment % by segment, event fire counts, p-value / CI
• Replication result: same metric in follow-up and directionality

Common mistakes & fixes

• Small subgroup N — fix: do a quick replication or require larger N threshold.
• Broken instrumentation — fix: compare raw event counts and timestamps; reprocess if needed.
• Multiple comparisons — fix: pre-specify top 1–2 hypotheses and treat others as exploratory.

1-week action plan (crystal clear)

1. Day 1: run slices, compute conversions & Ns, flag candidates.
2. Day 2: run balance check (variant % by segment) and instrumentation check (event counts/timestamps).
3. Day 3: feed summary to AI prompt below for prioritized hypotheses and exact checks.
4. Day 4: design mobile-only replication with N target (e.g., 6–8k per arm) and stopping rule.
5. Days 5–7: launch replication, monitor daily but don’t stop early; evaluate at target N or after 2 weeks.

Copy-paste AI prompt (use after you have counts & rates)

“You are a pragmatic product manager. Summary: overall A: 12% (n=20,000), B: 14% (n=20,000). Mobile users A: 10% (n=8,000), B: 16% (n=8,200). Desktop users A: 13% (n=12,000), B: 12% (n=11,800). Provide: 1) three prioritized causal hypotheses for the mobile lift, 2) two exact validation checks I can run in a spreadsheet (formulas or steps), 3) a mobile-only A/B replication plan with suggested sample sizes and a stopping rule.”

Your move.

— Aaron

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