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Correlation vs Causation in Promotions

Easy

Statistics & Probability

Asked at 1 company1Hypothesis TestingCorrelationCausal Inference

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Problem

Business Context

A Product Growth Analyst at Didi Chuxing is reviewing whether exposure to a Didi Ride promo banner in the app causes higher same-day ride booking, or whether the observed relationship is only correlation driven by confounding factors such as user activity level.

Problem Statement

You are given aggregated data from 8 user segments. First, measure the association between promo exposure rate and same-day booking rate. Then evaluate whether that association supports a causal claim after accounting for average prior weekly app opens, a likely confounder.

Given Data

Segment	Promo Exposure Rate $x$	Same-Day Booking Rate $y$	Avg Prior Weekly App Opens $z$
1	0.10	0.050	1.0
2	0.20	0.070	2.0
3	0.30	0.090	3.0
4	0.40	0.110	4.0
5	0.50	0.130	5.0
6	0.60	0.150	6.0
7	0.70	0.170	7.0
8	0.80	0.190	8.0

Assume a significance level of $\alpha = 0.05$ .

Requirements

Compute the Pearson correlation between promo exposure rate and booking rate.
Test whether the correlation is significantly different from 0.
Fit a simple linear regression of booking rate on promo exposure rate.
Explain why the regression coefficient alone does not prove causation.
Refit the model conceptually after controlling for prior weekly app opens, and determine whether a causal interpretation is justified.
State what additional evidence or design would be needed to make a causal claim.

Assumptions

Each segment is independent.
Rates are measured without material logging error.
Linear association is a reasonable first-pass approximation.
Prior weekly app opens may affect both promo exposure and booking propensity.

Sample Data

Example 1

Input

{"alpha":0.05,"n_segments":8,"booking_rate":[0.05,0.07,0.09,0.11,0.13,0.15,0.17,0.19],"promo_exposure_rate":[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8],"prior_weekly_app_opens":[1,2,3,4,5,6,7,8]}

Output(none)

Problem

Business Context

Problem Statement

Given Data

Segment	Promo Exposure Rate $x$	Same-Day Booking Rate $y$	Avg Prior Weekly App Opens $z$
1	0.10	0.050	1.0
2	0.20	0.070	2.0
3	0.30	0.090	3.0
4	0.40	0.110	4.0
5	0.50	0.130	5.0
6	0.60	0.150	6.0
7	0.70	0.170	7.0
8	0.80	0.190	8.0

Assume a significance level of $\alpha = 0.05$ .

Requirements

Compute the Pearson correlation between promo exposure rate and booking rate.
Test whether the correlation is significantly different from 0.
Fit a simple linear regression of booking rate on promo exposure rate.
Explain why the regression coefficient alone does not prove causation.
Refit the model conceptually after controlling for prior weekly app opens, and determine whether a causal interpretation is justified.
State what additional evidence or design would be needed to make a causal claim.

Assumptions

Each segment is independent.
Rates are measured without material logging error.
Linear association is a reasonable first-pass approximation.
Prior weekly app opens may affect both promo exposure and booking propensity.

Sample Data

Example 1

Input

{"alpha":0.05,"n_segments":8,"booking_rate":[0.05,0.07,0.09,0.11,0.13,0.15,0.17,0.19],"promo_exposure_rate":[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8],"prior_weekly_app_opens":[1,2,3,4,5,6,7,8]}

Output(none)

Your answer

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Regression for Growth Driver EstimationMedium

A/B Test Design for Didi Rider CouponsMedium

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Correlation vs Causation in Promotions

Easy

Statistics & Probability

Asked at 1 company1Hypothesis TestingCorrelationCausal Inference

Also asked at

Problem

Business Context

Problem Statement

Given Data

Segment	Promo Exposure Rate $x$	Same-Day Booking Rate $y$	Avg Prior Weekly App Opens $z$
1	0.10	0.050	1.0
2	0.20	0.070	2.0
3	0.30	0.090	3.0
4	0.40	0.110	4.0
5	0.50	0.130	5.0
6	0.60	0.150	6.0
7	0.70	0.170	7.0
8	0.80	0.190	8.0

Assume a significance level of $\alpha = 0.05$ .

Requirements

Compute the Pearson correlation between promo exposure rate and booking rate.
Test whether the correlation is significantly different from 0.
Fit a simple linear regression of booking rate on promo exposure rate.
Explain why the regression coefficient alone does not prove causation.
Refit the model conceptually after controlling for prior weekly app opens, and determine whether a causal interpretation is justified.
State what additional evidence or design would be needed to make a causal claim.

Assumptions

Each segment is independent.
Rates are measured without material logging error.
Linear association is a reasonable first-pass approximation.
Prior weekly app opens may affect both promo exposure and booking propensity.

Sample Data

Example 1

Input

{"alpha":0.05,"n_segments":8,"booking_rate":[0.05,0.07,0.09,0.11,0.13,0.15,0.17,0.19],"promo_exposure_rate":[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8],"prior_weekly_app_opens":[1,2,3,4,5,6,7,8]}

Output(none)

Problem

Business Context

Problem Statement

Given Data

Segment	Promo Exposure Rate $x$	Same-Day Booking Rate $y$	Avg Prior Weekly App Opens $z$
1	0.10	0.050	1.0
2	0.20	0.070	2.0
3	0.30	0.090	3.0
4	0.40	0.110	4.0
5	0.50	0.130	5.0
6	0.60	0.150	6.0
7	0.70	0.170	7.0
8	0.80	0.190	8.0

Assume a significance level of $\alpha = 0.05$ .

Requirements

Compute the Pearson correlation between promo exposure rate and booking rate.
Test whether the correlation is significantly different from 0.
Fit a simple linear regression of booking rate on promo exposure rate.
Explain why the regression coefficient alone does not prove causation.
Refit the model conceptually after controlling for prior weekly app opens, and determine whether a causal interpretation is justified.
State what additional evidence or design would be needed to make a causal claim.

Assumptions

Each segment is independent.
Rates are measured without material logging error.
Linear association is a reasonable first-pass approximation.
Prior weekly app opens may affect both promo exposure and booking propensity.

Sample Data

Example 1

Input

{"alpha":0.05,"n_segments":8,"booking_rate":[0.05,0.07,0.09,0.11,0.13,0.15,0.17,0.19],"promo_exposure_rate":[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8],"prior_weekly_app_opens":[1,2,3,4,5,6,7,8]}

Output(none)

Your answer

Try one AI text evaluation on us

Get structured feedback, scored against a 4-axis rubric. Premium unlocks unlimited.

0 wordstarget ~200

Up next

Regression for Growth Driver EstimationMedium

A/B Test Design for Didi Rider CouponsMedium

Correlation vs Causation in MarketingEasy

Next question