Google·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Interviewed for a data scientist role at Google and got hit with a pretty deep causal inference question about weather and mental health. Not a typical metrics or A/B testing setup, which threw me a bit.

Questions Asked (1)

How would you design a study to estimate the causal effect of weather conditions on mental health outcomes? Walk through your study design choices, the confounders you'd worry about (like seasonality, geography, and socioeconomic status), how you'd handle them, and how you'd validate and interpret your findings.

A/B Testing & ExperimentationProduct Analytics & MetricsTechnical Trade-offs

Author's notes

I started with observational study design and immediately flagged seasonality as the big confounder, which felt right, but then I kind of stumbled when they pushed on geography and SES together.

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AI HintsAI Generated

Suggested Approach

Start by framing the causal question and discussing why a randomized experiment is infeasible, then propose a quasi-experimental design like instrumental variables or difference-in-differences. Walk through identification assumptions, confounder control, robustness checks, and how you'd interpret effect sizes for product or policy decisions.

Pro tip: Acknowledge that weather is not randomly assigned and that unobserved time-varying confounders (e.g., economic shocks) can bias estimates; propose using high-frequency fixed effects and placebo tests to strengthen causal claims.

1. Define the causal estimand and identify challenges

Clarify the treatment (e.g., daily sunlight hours), outcome (e.g., PHQ-9 scores), and target population. Explain why randomization is impossible and outline the fundamental problem of causal inference.

2. Choose a quasi-experimental design

Select an identification strategy such as instrumental variables (e.g., random weather shocks), difference-in-differences (e.g., comparing regions before/after weather events), or regression discontinuity (e.g., threshold in sunlight). Justify why it addresses confounding.

3. Address confounders and specify the model

List confounders like seasonality, geography, and socioeconomic status. Describe how to handle them: include fixed effects (individual, time, location), control for time-varying covariates, and use methods like matching or propensity scores if needed.

4. Validate assumptions and robustness

Test identification assumptions (e.g., exclusion restriction, parallel trends) using placebo tests, falsification checks, and sensitivity analyses. Consider alternative specifications and data sources to rule out spurious correlations.

5. Interpret findings and discuss limitations

Quantify the effect size with confidence intervals, discuss practical significance, and acknowledge remaining threats to validity (e.g., measurement error, external validity). Suggest how results could inform interventions or further research.

Key Points to Mention

Use of instrumental variables (e.g., random weather shocks) to isolate exogenous variation in weather.
Inclusion of high-dimensional fixed effects (e.g., individual, location, time) to control for unobserved heterogeneity.
Difference-in-differences design comparing mental health outcomes before and after weather events across regions.
Handling seasonality by including month or week fixed effects or using harmonic terms.
Controlling for socioeconomic status through individual-level covariates or area-level deprivation indices.
Robustness checks like placebo tests (e.g., using future weather as a placebo) and sensitivity to model specification.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.