Experience how UM-Model 1's causal reasoning outperforms pattern matching in critical decision scenarios. Each simulation demonstrates the failure modes of correlation-based AI and the superiority of causal inference.
Drug effectiveness with confounding bias
A hospital observes that patients taking Drug A have 20 mmHg lower blood pressure than those not taking it. Should they prescribe Drug A to all hypertensive patients?
model.fit(X=drug_usage, y=blood_pressure) prediction = model.predict(drug_A=True) # Output: "Drug A reduces BP by 20 mmHg"
Healthier patients are more likely to receive Drug A AND have lower BP independently. The correlation is spurious.
graph = discover_causal_structure(data)
# Identifies: Health Status → Drug A
# Health Status → BP
confounders = identify_confounders("Drug A", "BP")
causal_effect = compute_do_calculus(
P(BP | do(Drug A = True)),
adjusting_for=confounders
)
# Output: "Drug A reduces BP by only 3 mmHg"
Adjusts for health status confounder, revealing true causal effect of 3 mmHg vs. spurious 20 mmHg.
Separating causal effect from seasonal correlation
E-commerce company spent $1M on TV campaign. Sales increased 20% during campaign. Was the campaign effective?
correlation = compute_correlation(tv_spend, sales) # Output: r = 0.78 attributed_sales = sales_increase * correlation # Output: "Campaign caused $780K in sales" # ROI: 234%
Holiday season, economic growth, and competitor closure all occurred during campaign. Massively overestimating campaign impact.
graph = discover_causal_structure(data)
# Season → Sales (0.65)
# Economy → Sales (0.45)
# Competitor → Sales (0.30)
# Campaign → Sales (0.15)
result = counterfactual_inference(
variable="TV Campaign",
actual=1.0,
counterfactual=0.0,
context={"Season": "Holiday", ...}
)
# Output: "Without campaign: $5.52M"
# True effect: $480K (8%)
# True ROI: 120%
Separates campaign effect from seasonal and economic factors through "what if" reasoning.
Distribution shift and causal physics understanding
Self-driving car approaching intersection at 45 mph. Pedestrian detected 30 feet ahead. Road is wet. Should the car brake or swerve?
action = model.predict(
speed=45,
pedestrian_distance=30,
road_type="urban"
)
# Output: "Brake" (70% success in training)
Current conditions (wet road, high speed, short distance) not in training distribution. Physics: Stopping distance on wet road = 84 feet > 30 feet available.
graph = build_causal_physics_model()
# Speed → Kinetic Energy → Stopping Distance
# Road Friction → Braking Force → Stopping Distance
brake_outcome = counterfactual_inference(
variable="Action",
counterfactual="Brake",
context={"Speed": 45, "Friction": 0.4, "Distance": 30}
)
# P(Collision | do(Brake)) = 0.92
swerve_outcome = counterfactual_inference(
variable="Action",
counterfactual="Swerve",
context={...}
)
# P(Collision | do(Swerve)) = 0.08
Understands stopping distance physics: d = v²/(2μg). Recognizes wet road makes braking impossible. Evaluates swerve through causal simulation.
Selection bias in credential-based hiring
Company hiring software engineers. Historical data shows employees from top universities perform 30% better. Should they only hire from elite schools?
model.fit(X=university_tier, y=performance) prediction = model.predict(university="Top") # Output: "Top university → 8.5/10 performance"
Top universities attract already-talented students. The credential is a proxy for pre-existing ability, not a cause of performance. Missing diverse talent.
graph = discover_causal_structure(data)
# Innate Ability → University Tier
# Innate Ability → Performance
# Skills → Performance (causal)
confounders = identify_confounders("University", "Performance")
# Finds: Innate Ability is confounder
causal_effect = measure_skill_causation(
adjusting_for=["Innate_Ability"]
)
# Output: "University has 0.15 causal effect"
# Skills have 0.72 causal effect
Measures actual causal impact of skills on performance, removing credential bias.