ML Interview Q Series: Does lower satisfaction among users enabling location-sharing imply the feature caused their dissatisfaction?
📚 Browse the full ML Interview series here.
Comprehensive Explanation
The question highlights a crucial distinction between correlation and causation. Observing that individuals who use the location-sharing feature are less happy does not automatically establish the feature as the direct cause of their dissatisfaction. There could be many underlying reasons, such as self-selection bias, additional factors that correlate with the willingness to share location, or the presence of confounding variables (for example, users who travel frequently might use the feature more but also be more critical of certain app functionalities).
Why Correlation Is Not Always Causation
When a survey shows that a specific group (those who turn on location sharing) reports lower satisfaction, we only know that two events are related (using location sharing and being less satisfied). This relationship might arise from:
Self-selection: Users prone to privacy concerns or high expectations may be more likely to notice flaws in an app and thus also more likely to participate in the location-sharing feature with some caution or skepticism.
Confounding variables: Users who engage more deeply with an app might explore more features (like location sharing) and simultaneously find more points of critique.
Measurement bias: Survey responses might differ based on the attitudes or demographics of those who choose to take part in the new feature.
Approaches To Test For Causation
A strong way to determine whether location sharing truly makes people less happy is to move from observational data to experimental data. The gold standard approach involves randomizing who gets access or encouragement to use a feature, then measuring the difference in outcomes. In causal inference terms, one might attempt to measure the “Average Treatment Effect” (ATE).
Where:
Y(1) is the outcome (e.g., reported happiness level) if a user is assigned to the “treatment” group that uses the feature.
Y(0) is the outcome if a user is assigned to the “control” group that does not use or is not prompted to use the feature.
E[·] denotes the expected value (average) of the outcome.
By randomly assigning users to the feature or to a no-feature group, we minimize systematic differences between the groups aside from the treatment itself. If this randomization is successful, any significant difference in average happiness can be more confidently attributed to the feature.
Example: Hypothetical A/B Test Implementation
import numpy as np
from scipy.stats import ttest_ind
# Let's say we randomly assign users to two groups:
# group_A uses the location feature (treatment),
# group_B does not use it (control).
# We measure their happiness on a scale 1-10.
# Randomly generate some example data
np.random.seed(42)
group_A_happiness = np.random.normal(loc=7.0, scale=1.0, size=500) # location-sharers
group_B_happiness = np.random.normal(loc=7.2, scale=1.0, size=500) # non-location-sharers
# Perform a two-sample t-test
t_stat, p_value = ttest_ind(group_A_happiness, group_B_happiness)
print("Mean of group A (treatment):", np.mean(group_A_happiness))
print("Mean of group B (control):", np.mean(group_B_happiness))
print("T-statistic:", t_stat)
print("P-value:", p_value)
In this example, we simulate and measure the average happiness in each group. If the difference in means is statistically significant (with a sufficiently low p-value) and random assignment is valid, we can infer a causal relationship. If the difference is not significant, we do not have evidence to assert that location sharing makes users less happy.
Follow-Up Questions
If the survey just shows correlation, how can we be sure there’s no unseen variable driving the results?
Confounding variables can make it appear that using the feature causes lower satisfaction, whereas the real driver might be something else. One way to reduce the influence of unknown factors is random assignment, which is the essence of A/B testing. If randomization is not feasible, advanced observational techniques like propensity score matching can help approximate randomization by pairing users with similar characteristics except for whether they use the feature.
Could user demographics or usage patterns affect the perception of this feature?
Yes, different segments of users can have different preferences and behaviors. For instance, privacy-minded users might be especially critical of any “tracking” feature, so their self-reported happiness could be lower for reasons not related to the feature’s mechanics but rather their general apprehension about sharing data. Segmenting analyses by demographic or usage pattern can help uncover such nuances and reveal whether the drop in satisfaction is universal or localized to specific user groups.
How do we handle ethical or privacy concerns in testing a location-sharing feature?
When dealing with features that involve sensitive data like location, one must ensure informed user consent and adhere to privacy regulations. Ethical considerations might prevent forcing users into location sharing, so purely random assignment might be impossible. Instead, we could randomize invitations or nudges to enable the feature, rather than forcibly enabling it. All the while, data handling must be transparent and compliant with data protection laws.
What if many users who enable location sharing do not respond to surveys or do so differently?
Response bias is a common concern. Users who are unhappy may be more vocal and more likely to complete surveys, skewing results. Adjusting for survey response rates or weighting responses by known user characteristics can help mitigate these biases. Where possible, measuring happiness through indirect metrics (e.g., app engagement, churn, or usage patterns) can complement or validate self-reported surveys.
How can we further validate that the feature isn’t just correlated but truly causing dissatisfaction?
Beyond A/B testing and controlling for confounders, one could attempt time-series analyses where we monitor user happiness before and after they adopt the feature. If we see a clear drop right after adoption (and not before), that strengthens the case for causation. However, only a well-designed experiment or a sequence of corroborating studies can confirm cause-effect with high confidence.
By addressing these follow-up questions thoroughly, you demonstrate a solid understanding of the potential pitfalls in inferring causation from correlations and the importance of designing proper experiments or analyses to identify the true impact of an optional feature like location sharing.
Below are additional follow-up questions
How might user churn or changes in long-term behavior affect the interpretation of dissatisfaction?
One subtle challenge is that users who strongly dislike the location-sharing feature might abandon the app entirely, leaving behind a pool of users whose average satisfaction could appear artificially high. Over time, this self-selection could make the feature’s impact look less severe because the most dissatisfied individuals have already left. On the other hand, newer users might join already expecting location sharing as standard, thus bringing different baseline expectations. Measuring satisfaction only at a single time point fails to capture this evolving user population. Analyzing longitudinal data to see how satisfaction scores and churn rates change over time is critical. If churn correlates strongly with the introduction or usage of location sharing, there is a stronger indication that the feature may be contributing to overall dissatisfaction.
Could differences in how intensely or frequently users leverage the feature reveal more nuanced insights?
Many features, including location sharing, have gradients of usage rather than a simple on/off switch. Some users might use location sharing only when traveling, others might keep it on permanently, while some might never enable it. Each usage pattern could yield distinct satisfaction outcomes. Those who only enable location sharing occasionally may find it useful in specific scenarios, leading to moderate or even positive app evaluations, while heavy users might experience more performance or privacy concerns. Fine-grained tracking of usage frequency, combined with satisfaction metrics, helps reveal these complexities. Segmenting the user base by usage intensity might uncover subgroups for which the feature is beneficial or detrimental in different ways.
Are there external factors, such as concurrent events or competitor features, that could influence user satisfaction independently?
Users’ perceptions could be shaped by outside influences rather than just the new feature. For instance, if there is a growing privacy-related controversy elsewhere in the tech industry, public sentiment might shift against any form of location-based services, causing a drop in satisfaction unrelated to the actual function of the feature within this particular app. Similarly, a competitor’s recent release of a highly polished location-based tool might raise user expectations. When examining a drop in satisfaction, it is important to examine external events, industry trends, and competitor actions to ensure that the correlation with a new feature is not coincidental.
Could the design or user interface of the location-sharing feature itself cause friction or dissatisfaction, rather than the concept of location sharing?
Sometimes users might appreciate the core utility of a feature but dislike how it is implemented. For example, the feature’s interface might be confusing, slow, or cluttered with too many permission dialogues. Battery drain or excessive notifications can also irritate users. The dissatisfaction might arise from poor implementation details rather than from location sharing as a concept. Thorough user experience (UX) testing, user feedback sessions, and data on app performance metrics can reveal if user frustration stems from these design and implementation factors. If so, refining the user interface and optimizing performance might resolve the dissatisfaction without removing the feature.
In what ways could user distrust or privacy anxieties amplify negative sentiment independent of actual data usage?
Users may feel uncomfortable if they perceive that the app is collecting more data than necessary, even if the app follows strict privacy guidelines. The “fear of being tracked” can overshadow any practical benefits. This is especially critical if the app’s privacy policy, data retention, or handling is not clearly communicated. Even if no real privacy violations occur, poor communication can trigger suspicion or frustration, causing dissatisfaction to skyrocket for reasons unrelated to actual functionality. Transparent data-handling policies and user education about what is (and isn’t) done with location data could mitigate these anxieties.
How does the method of asking about satisfaction influence the likelihood of detecting a causal relationship?
Survey design and delivery method can bias results. Leading questions about privacy or invasive app behavior can predispose users to respond negatively about the feature. The timing of the survey might also matter—if the survey appears immediately after prompting users to share their location, dissatisfaction might be higher than if the survey appears at a random time. Additionally, if the survey is too long or confusing, fewer users might respond, creating self-selection biases that skew the overall results. Designing surveys with neutral language, clear instructions, and randomization of question order can help ensure that the measured dissatisfaction is more genuinely related to the feature’s experience.
Could certain subsets of the user population, such as power users or business travelers, provide misleading overall satisfaction metrics?
Different user groups might use the feature with unique motivations and expectations. Business travelers might rely on location sharing to coordinate with colleagues and thus be more willing to tolerate potential drawbacks if the feature satisfies a business need. Conversely, casual users might enable location sharing experimentally and become annoyed if it does not add immediate value, leading to negative overall impressions. If a disproportionate number of survey respondents come from one user group, the data might wrongly generalize their experience to the entire population. Stratifying or weighting responses based on user profiles—e.g., usage intensity, frequency of travel, type of network usage—can clarify which groups find real benefit and which groups are dissatisfied.
How might partial rollouts or A/B tests fail to fully capture user-level and network-level effects?
When only some users receive the location-sharing feature, there is a risk that the overall ecosystem effect remains hidden. For instance, location sharing could become more valuable if many friends also have it enabled. Alternatively, if the feature creates clutter or notifications that spill over into communication channels, non-participants might also become dissatisfied. A carefully designed A/B test might need to account for network-level behaviors where the sum of participants’ experiences differs from the individual-level results. Randomizing at the cluster or social-group level, rather than purely at the individual level, may be necessary to grasp the broader implications. Without accounting for these network effects, the results might underestimate or overestimate the feature’s impact on satisfaction.