The Causal Confounder - difference-in-differences practice
Pre-trend gap: 0.0 per month. They were moving together before the policy, so the design is admissible - the levels differ, but the trends match.
What does differencing the differences give you?
Southvale tells you what Northgate would have done anyway. Subtract it out.
About The Causal Confounder
Estimate damages with difference-in-differences, then work out which of three facts from discovery biases the estimate.
You are the testifying economist in a damages case. Northgate Retail (the treated region, where the defendant's pricing policy took effect) sold 520k units a month before the policy and 400k after. Southvale (the untouched control) went from 500k to 460k over the same window. Pre-period months are shown for both regions so you can check parallel trends: both were rising at the same rate before the policy, so the design is admissible.
Phase 1 asks for the naive difference-in-differences estimate: the treated group's change minus the control group's change, in thousands of units. Phase 2 presents three facts from discovery - a national fuel surcharge that hit every region, a warehouse fire that hit only Northgate during the post period, and the fact that Northgate has always sold about 20k more than Southvale - and asks which single one biases the DiD estimate.
Phase 3 gives you the fire's documented impact (30k units) and asks for the adjusted causal effect: the naive DiD with the treated-only shock stripped out. Numeric answers are accepted within 3 (thousand units) and each question locks after one check; you can restart the case at the end.
Why quant interviews test this
Difference-in-differences is the workhorse of economic consulting - damages estimation, antitrust impact studies, labor and policy litigation - and interviews test exactly the sequence this game runs: set up the 2x2, state the parallel-trends assumption, compute the estimate, then hunt for what else could explain it. The confounder step is where candidates separate: anyone can subtract two numbers, but naming which facts bias the design and which cancel shows you understand the identification behind the formula.
Expect follow-ups of the form "opposing counsel says the regions were always different - does that kill your estimate?" The trained answer: no, level differences cancel; what would kill it is a treated-only shock inside the post window, and here is how I would test for one.
The The Causal Confounder guide covers how scoring works, the strategy that wins, a worked example and the mistakes most players make.
More Economics games
- The Antitrust Simulator - Define the relevant market for a merger, compute the HHI delta, and file the verdict the guidelines support.
- All Economics practice
- Every game guide