gh-lyndonkl-claude/skills/hypotheticals-counterfactuals/resources/methodology.md

Hypotheticals and Counterfactuals: Advanced Methodology

This document provides advanced techniques for counterfactual reasoning, scenario planning, pre-mortem analysis, and extracting actionable insights from alternative futures.

Table of Contents

1. Counterfactual Reasoning
2. Scenario Planning Techniques
3. Extracting Insights from Scenarios
4. Monitoring and Adaptation
5. Advanced Topics

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1. Counterfactual Reasoning

What Is Counterfactual Reasoning?

Counterfactual reasoning asks: "What would have happened if X had been different?" It's a form of causal inference through imagined alternatives.

Core principle: To understand causality, imagine the world with one factor changed and trace the consequences.

Example:

• Actual: Startup raised $5M Series A → burned through runway in 14 months → failed to reach profitability
• Counterfactual: "What if we had raised $3M instead?"
  • Hypothesis: Smaller team (8 vs 15 people), lower burn, forced focus on revenue, reached profitability in 12 months
  • Reasoning: Constraint forces discipline. Without $5M runway, couldn't afford large team. Would prioritize revenue over growth.
  • Insight: Raising more money enabled premature scaling. Constraint would have been beneficial.

The Minimal Rewrite Principle

When constructing counterfactuals, change as little as possible to isolate the causal factor.

Bad counterfactual (too many changes):

• "What if we had raised $3M AND competitor had failed AND market had doubled?"
• Problem: Can't tell which factor caused different outcome

Good counterfactual (minimal change):

• "What if we had raised $3M (all else equal)?"
• Isolates the funding amount as causal variable

Technique: Hold everything constant except the factor you're testing. This reveals whether that specific factor was causal.

Constructing Plausible Counterfactuals

Not all "what ifs" are useful. Counterfactuals must be plausible given what was known/possible at the time.

Plausible: "What if we had launched in EU first instead of US?"

• This was a real option available at decision time

Implausible: "What if we had magically known the pandemic was coming?"

• Requires impossible foreknowledge

Test for plausibility: Could a reasonable decision-maker have chosen this alternative given information available at the time?

Specifying Causal Mechanisms

Don't just state outcome; explain HOW the change leads to different result.

Weak counterfactual: "Sales would be 2× higher" Strong counterfactual: "Sales would be 2× higher because lower price ($50 vs $100) → 3× higher conversion rate (15% vs 5%) → more customers despite 50% lower margin per customer → net revenue impact +2×"

Framework for causal chains:

1. Initial change: What's different? (e.g., "Price is $50 instead of $100")
2. Immediate effect: What happens next? (e.g., "Conversion rate increases from 5% to 15%")
3. Secondary effects: What follows? (e.g., "Customer volume triples")
4. Final outcome: Net result? (e.g., "Revenue doubles despite lower margin")

Using Counterfactuals for Learning

Post-decision counterfactual analysis:

After a decision plays out, ask:

1. What did we decide? (Actual decision)
2. What was the outcome? (Actual result)
3. What else could we have done? (Alternative decision)
4. What would have happened? (Counterfactual outcome)
5. What was the key causal factor? (Insight for future)

Example: Hired candidate A (strong technical, weak communication) → struggled, left after 6 months. Counterfactual: B (moderate technical, strong communication) would have stayed longer, collaborated better. Insight: For this role, communication > pure technical skill.

Avoiding Hindsight Bias

Hindsight bias: "I knew it all along" — outcome seems inevitable after the fact.

Problem: Makes counterfactual analysis distorted. "Of course it failed, we should have known."

Mitigation:

• Re-inhabit decision context: What information was available then (not now)?
• List alternatives considered: What options were on the table at the time?
• Acknowledge uncertainty: How predictable was outcome given information available?

Technique: Write counterfactual analysis in past tense but from perspective of decision-maker at the time, without benefit of hindsight.

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2. Scenario Planning Techniques

Three-Scenario Framework

Structure: Optimistic, Baseline, Pessimistic futures

When to use: General strategic planning, forecasting, resource allocation

Process:

1. Define time horizon: 6 months? 1 year? 3 years? 5 years?

   • Shorter horizons: More specific, quantitative
   • Longer horizons: More qualitative, exploratory

2. Identify key uncertainties: What 2-3 factors most shape the future?

   • Market adoption rate
   • Competitive intensity
   • Regulatory environment
   • Technology maturity
   • Economic conditions

3. Develop three scenarios:

   • Optimistic (15-30% probability): Best-case assumptions on key uncertainties
   • Baseline (40-60% probability): Expected-case, extrapolating current trends
   • Pessimistic (15-30% probability): Worst-case assumptions on key uncertainties

4. Describe each vividly: Write 2-4 paragraph narrative making each world feel real

5. Extract implications: What should we do in each scenario?

Example (SaaS startup, 2-year horizon):

Key uncertainties: (1) Market adoption rate, (2) Competition intensity

Optimistic scenario (20% probability): "Market Leader"

• Adoption: 40% market share, viral growth, $10M ARR
• Competition: Weak, no major new entrants
• Drivers: Product-market fit strong, word-of-mouth, early mover advantage
• Implications: Invest heavily in scale infrastructure, expand to adjacent markets

Baseline scenario (60% probability): "Steady Climb"

• Adoption: 15% market share, steady growth, $3M ARR
• Competition: Moderate, 2-3 well-funded competitors
• Drivers: Expected adoption curve, competitive but differentiated
• Implications: Focus on core product, maintain burn discipline, build moat

Pessimistic scenario (20% probability): "Survival Mode"

• Adoption: 5% market share, slow growth, $500k ARR
• Competition: Intense, major player launches competing product
• Drivers: Slow adoption, strong competition, pivot needed
• Implications: Cut burn, extend runway, explore pivot or acquisition

2×2 Scenario Matrix

Structure: Two key uncertainties create four quadrants (scenarios)

When to use: When two specific uncertainties dominate strategic decision

Process:

1. Identify two critical uncertainties: Factors that:

   • Are genuinely uncertain (not predictable)
   • Have major impact on outcomes
   • Are independent (not correlated)

2. Define axes extremes:

   • Uncertainty 1: [Low extreme] ←→ [High extreme]
   • Uncertainty 2: [Low extreme] ←→ [High extreme]

3. Name four quadrants: Give each world a memorable name

4. Develop narratives: Describe what each world looks like

5. Identify strategic implications: What works in each quadrant?

Example (Market entry decision):

Uncertainty 1: Regulatory environment (Strict ←→ Loose) Uncertainty 2: Market adoption rate (Slow ←→ Fast)

	Slow Adoption	Fast Adoption
Strict Regulation	"Constrained Growth": Premium focus, compliance differentiator	"Regulated Scale": Invest in compliance infrastructure early
Loose Regulation	"Patient Build": Bootstrap, iterate slowly	"Wild West Growth": Fast growth, grab market share

Strategic insight: Common actions (build product), hedges (low burn), options (compliance prep), monitoring (regulation + adoption)

Cone of Uncertainty

Structure: Range of outcomes widens over time

When to use: Long-term planning (5-10+ years), high uncertainty domains (technology, policy)

Visualization:

Present → 1 year: Narrow cone (±20%)
        → 3 years: Medium cone (±50%)
        → 5 years: Wide cone (±100%)
        → 10 years: Very wide cone (±200%+)

Technique:

1. Start with trend: Current trajectory (e.g., "10% annual growth")
2. Add uncertainty bands: Upper and lower bounds that widen over time
3. Identify branch points: Key decisions or events that shift trajectory
4. Track leading indicators: Signals that show which path we're on

Example (Revenue forecasting):

• Year 1: $1M ± 20% ($800k - $1.2M) — narrow range, short-term visibility
• Year 3: $3M ± 50% ($1.5M - $4.5M) — wider range, product-market fit uncertain
• Year 5: $10M ± 100% ($5M - $20M) — very wide range, market evolution unknown

Pre-Mortem Process (Prospective Hindsight)

What is it?: Imagine future failure, work backward to identify causes

Why it works: "Prospective hindsight" — imagining outcome has occurred unlocks insights impossible from forward planning

Research foundation: Gary Klein, "Performing a Project Premortem" (HBR 2007)

6-Step Process:

Step 1: Set the scene

• Future date: "It is [6 months / 1 year / 2 years] from now..."
• Assumed outcome: "...and the [project has failed completely / decision was disastrous]."
• Make it vivid: "The product has been shut down. The team disbanded. We lost $X."

Step 2: Individual brainstorm (5-10 minutes, silent)

• Each person writes 3-5 reasons WHY it failed
• Silent writing prevents groupthink
• Encourage wild ideas, non-obvious causes

Step 3: Round-robin sharing

• Each person shares one reason (rotating until all shared)
• No debate yet, just capture ideas
• Scribe records all items

Step 4: Consolidate and cluster

• Group similar causes together
• Look for themes (technical, market, team, execution, external)

Step 5: Vote on top risks

• Dot voting: Each person gets 3-5 votes
• Distribute votes across risks
• Identify top 5-7 risks by vote count

Step 6: Develop mitigations

• For each top risk, assign:
  • Mitigation action: Specific step to prevent or reduce risk
  • Owner: Who is responsible
  • Deadline: When mitigation must be in place
  • Success metric: How to know mitigation worked

Pre-mortem psychology:

• Permission to dissent: Failure assumption gives license to voice concerns without seeming negative
• Cognitive relief: Easier to imagine specific failure than abstract "what could go wrong?"
• Team alignment: Surfaces hidden concerns before they become real problems

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3. Extracting Insights from Scenarios

Moving from Stories to Actions

Scenarios are useless without actionable implications. After developing scenarios, ask:

Core questions:

1. What should we do regardless of which scenario unfolds? (Common actions)
2. What hedges should we take against downside scenarios? (Risk mitigation)
3. What options should we create for upside scenarios? (Opportunity capture)
4. What should we monitor to track which scenario is unfolding? (Leading indicators)

Identifying Common Actions

Common actions ("no-regrets moves"): Work across all scenarios

Technique: List actions that make sense in optimistic, baseline, AND pessimistic scenarios

Example (Product launch scenarios):

Scenario	Build Core Product	Hire Marketing	Raise Series B
Optimistic	✓ Essential	✓ Essential	✓ Essential
Baseline	✓ Essential	✓ Essential	△ Maybe
Pessimistic	✓ Essential	△ Maybe	✗ Too risky

Common actions: Build core product, hire marketing (work in all scenarios) Not common: Raise Series B (only makes sense in optimistic/baseline)

Designing Hedges

Hedges: Actions that reduce downside risk if pessimistic scenario unfolds

Principle: Pay small cost now to protect against large cost later

Examples:

• Pessimistic scenario: "Competitor launches free version, our revenue drops 50%"

• Hedge: Keep burn low, maintain 18-month runway (vs. 12-month)

  • Cost: Hire 2 fewer people now
  • Benefit: Survive revenue shock if it happens

• Pessimistic scenario: "Regulatory crackdown makes our business model illegal"

• Hedge: Develop alternative revenue model in parallel

  • Cost: 10% of eng time on alternative
  • Benefit: Can pivot quickly if regulation hits

Hedge evaluation: Compare cost of hedge vs. expected loss × probability

• Hedge cost: $X
• Loss if scenario occurs: $Y
• Probability of scenario: P
• Expected value of hedge: (P × $Y) - $X

Creating Options

Options: Prepare to capture upside if optimistic scenario unfolds, without committing resources now

Real options theory: Create flexibility to make future decisions when more information available

Examples:

• Optimistic scenario: "Adoption faster than expected, enterprise demand emerges"

• Option: Design product architecture with enterprise features in mind (multi-tenancy, SSO hooks), but don't build until demand confirmed

  • Low cost now: Design decisions
  • High value later: Fast enterprise launch if demand materializes

• Optimistic scenario: "International markets grow 3× faster than expected"

• Option: Hire one person with international experience, build relationships with international partners

  • Low cost now: One hire
  • High value later: Quick international expansion if opportunity emerges

Defining Leading Indicators

Leading indicators: Early signals that show which scenario is unfolding

Characteristics of good leading indicators:

• Observable: Can be measured objectively
• Early: Visible before scenario fully plays out (6+ months advance notice)
• Actionable: If indicator triggers, we know what to do

Example (Market adoption scenarios):

Scenario	Leading Indicator	Threshold	Action if Triggered
Optimistic	Monthly adoption rate	>20% MoM for 3 months	Accelerate hiring, raise capital
Baseline	Monthly adoption rate	10-20% MoM	Maintain plan
Pessimistic	Monthly adoption rate	<10% MoM for 3 months	Cut burn, explore pivot

Monitoring cadence: Review indicators monthly or quarterly, update scenario probabilities based on new data

Decision Points and Trigger Actions

Decision points: Pre-defined thresholds that trigger specific actions

Format: "If [indicator] crosses [threshold], then [action]"

Examples:

• "If monthly churn rate >8% for 2 consecutive months, then launch retention task force"
• "If competitor raises >$50M, then accelerate roadmap and increase marketing spend"
• "If regulation bill passes committee vote, then begin compliance implementation immediately"

Benefits:

• Pre-commitment: Decide now what to do later, avoids decision paralysis in moment
• Speed: Trigger action immediately when condition met
• Alignment: Team knows what to expect, can prepare

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4. Monitoring and Adaptation

Tracking Which Scenario Is Unfolding

Reality ≠ any single scenario: Real world is blend of scenarios, or something unexpected

Monitoring approach:

1. Quarterly scenario review: Update probabilities based on new evidence
2. Indicator dashboard: Track 5-10 leading indicators, visualize trends
3. Surprise tracking: Log unexpected events not captured by scenarios

Example dashboard:

Indicator	Optimistic	Baseline	Pessimistic	Current	Trend
Adoption rate	>20% MoM	10-20%	<10%	15%	↑
Churn rate	<3%/mo	3-5%	>5%	4%	→
Competitor funding	<$20M	$20-50M	>$50M	$30M	↑
NPS	>50	30-50	<30	45	↑

Interpretation: Trending optimistic (adoption, NPS), watch competitor funding

Updating Scenarios

When to update:

• Scheduled: Quarterly reviews
• Triggered: Major unexpected event (pandemic, regulation, acquisition, etc.)

Update process:

1. Review what happened: What changed since last review?
2. Update probabilities: Which scenario looking more/less likely?
3. Revise scenarios: Do scenarios still capture range of plausible futures? Add new ones if needed
4. Adjust actions: Change hedges, options, or common actions based on new information

Example: Before pandemic: Optimistic 20%, Baseline 60%, Pessimistic 20%. After: Add "Remote-first world" (30%), reduce Baseline to 40%. Action: shift from office expansion to remote tooling.

Dealing with Surprises

Black swans: High-impact, low-probability events not captured by scenarios (Taleb)

Response protocol:

1. Acknowledge: "This is outside our scenarios"
2. Assess: How does this change the landscape?
3. Create emergency scenario: Rapid scenario development (hours/days, not weeks)
4. Decide: What immediate actions needed?
5. Update scenarios: Incorporate new uncertainty into ongoing planning

Example: COVID-19 lockdowns (not in scenarios) → Assess: dining impossible → Emergency scenario: "Delivery-only world" (6-12 mo) → Actions: pivot to takeout, renegotiate leases → Update: add "Hybrid dining" scenario

Scenario Planning as Organizational Learning

Scenarios as shared language: Team uses scenario names to communicate quickly

• "We're in Constrained Growth mode" → Everyone knows what that means

Scenario-based planning: Budgets, roadmaps reference scenarios

• "If we hit Optimistic scenario by Q3, we trigger hiring plan B"

Cultural benefit: Reduces certainty bias, maintains flexibility, normalizes uncertainty

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5. Advanced Topics

Counterfactual Probability Estimation

Challenge: How likely was counterfactual outcome?

Approach: Use base rates and analogies

1. Find analogous cases: What happened in similar situations?
2. Calculate base rate: Of N analogous cases, in how many did X occur?
3. Adjust for specifics: Is our case different? How?
4. Estimate probability range: Not point estimate, but range (40-60%)

Example: "What if we had launched in EU first?" — 20 similar startups: 8/20 chose EU-first (3/8 succeeded = 37.5%), 12/20 chose US-first (7/12 = 58%). Our product has EU features (+10%) → EU-first 35-50%, US-first 50-65%. Insight: US-first was better bet.

Scenario Narrative Techniques

Make scenarios memorable and vivid:

Technique 1: Use present tense

• Bad: "Adoption will grow quickly"
• Good: "It's January 2026. Our user base has grown 10× in 12 months..."

Technique 2: Add concrete details

• Bad: "Competition is intense"
• Good: "Three well-funded competitors (FundedCo with $50M Series B, StartupX acquired by BigTech, OpenSource Project with 10k stars) are fighting for same customers..."

Technique 3: Use personas/characters

• "Sarah, our typical customer (marketing manager at 50-person B2B SaaS company), now has five alternatives to our product..."

Technique 4: Include metrics

• "Monthly churn rate: 8%, NPS: 25, CAC: $500 (up from $200)"

Assumption Reversal for Innovation

Technique: Take core assumption, flip it, explore implications

Process:

1. List key assumptions: What do we take for granted?
2. Reverse each: "What if opposite is true?"
3. Explore plausibility: Could reversal be true?
4. Identify implications: What would we do differently?
5. Test: Can we experiment with reversal?

Examples:

Current Assumption	Reversed Assumption	Implications	Action
"Customers want more features"	"Customers want FEWER features"	Simplify product, remove rarely-used features, focus on core workflow	Survey: Would users pay same for product with 50% fewer features but better UX?
"Freemium is best model"	"Paid-only from day 1"	No free tier, premium positioning, higher LTV but lower top-of-funnel	Test: Launch premium SKU, measure willingness to pay
"We need to raise VC funding"	"Bootstrap and self-fund"	Slower growth, but control + profitability focus	Calculate: Can we reach profitability on current runway?

Timeboxing Scenario Work

Problem: Scenario planning can become endless theorizing

Solution: Timebox exercises

Suggested time budgets: Pre-mortem (60-90 min), Three scenarios (2-4 hrs), 2×2 matrix (3-5 hrs), Quarterly review (1-2 hrs)

Principle: Scenarios are decision tools, not academic exercises. Generate enough insight to decide, then act.

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Summary

Counterfactual reasoning reveals causality through minimal-change thought experiments. Focus on plausibility, specify mechanisms, avoid hindsight bias.

Scenario planning (three scenarios, 2×2 matrix, cone of uncertainty) explores alternative futures. Assign probabilities, make vivid, extract actions.

Extract insights by identifying common actions (no-regrets moves), hedges (downside protection), options (upside preparation), and leading indicators (early signals).

Monitor and adapt quarterly. Track indicators, update scenario probabilities, adjust strategy as reality unfolds. Treat surprises as learning opportunities.

Advanced techniques include counterfactual probability estimation, narrative crafting, assumption reversal, and rigorous timeboxing to avoid analysis paralysis.

The goal: Prepare for uncertainty, maintain strategic flexibility, and make better decisions by systematically exploring "what if?"