Instacart·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Instacart DS interview with a causal inference case study built around a real product scenario. Pretty technical for a single question but they gave you enough rope to either hang yourself or actually show some depth.

Questions Asked (1)

Instacart rolled out ultrafast delivery in Miami two months ago. How would you estimate the causal impact of this feature on order volume? Walk through how you'd pick a control geography, explain the mechanics of your chosen method, and if you go with a linear mixed-effects model, say which variables you'd treat as fixed vs. random.

A/B Testing & ExperimentationProduct Analytics & MetricsData Modeling

Author's notes

I went with synthetic control over DiD because Miami is kind of a weird market and finding a single clean parallel-trend city felt like wishful thinking.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by framing the problem as a causal inference question where randomization is absent, so you need a quasi-experimental design. Propose a difference-in-differences (DiD) approach with a carefully selected control geography, and then discuss how a linear mixed-effects model can account for repeated measures and clustering. Walk through the assumptions, mechanics, and how you'd validate the parallel trends assumption.

Pro tip: Emphasize the importance of pre-period data to test parallel trends and consider using synthetic control if a single control geography is insufficient. Also, mention that you'd check for spillover effects (e.g., customers in control areas ordering to Miami) and adjust for them if necessary.

1. Define the causal question and identify confounders

Clarify the treatment (ultrafast delivery in Miami) and outcome (order volume). Identify potential confounders like seasonality, local events, and marketing campaigns that could bias the estimate.

2. Select a control geography

Choose a control city that is similar to Miami in demographics, baseline order volume, and trends, but did not receive the treatment. Use matching or synthetic control methods to construct a comparable counterfactual.

3. Choose and explain the causal method

Propose difference-in-differences (DiD) to compare pre-post changes in Miami vs. control. Discuss assumptions (parallel trends) and how to test them. If using a linear mixed-effects model, specify fixed and random effects.

4. Specify the model and variables

For a linear mixed-effects model: fixed effects include treatment group, time period, and their interaction (DiD estimator), plus covariates like day-of-week, promotions, and weather. Random effects include geography-level intercepts and possibly time slopes to account for repeated measures.

5. Validate and interpret results

Check model assumptions, conduct placebo tests, and assess sensitivity to control choice. Interpret the treatment effect size and confidence intervals, and discuss limitations.

Key Points to Mention

Difference-in-differences (DiD) as the primary causal method
Parallel trends assumption and how to test it with pre-period data
Selection of control geography using matching or synthetic control
Linear mixed-effects model: fixed effects (treatment, time, interaction, covariates) and random effects (geography intercepts/slopes)
Potential spillover effects and how to mitigate them
Robustness checks: placebo tests, sensitivity analysis, and alternative control groups

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