Causal Toolkit — Case Studies

Reuses the exact same validated event/date as fed_pivot_tech, swapping the treated unit to XLU. Utilities are the textbook bond-proxy, rate-sensitive sector — this checks whether that theory actually shows up as a real synthetic-control gap, using the other 10 sectors as donors, or whether XLU just moved with the broader market like everything else that week. (Substituted for a literal single-day 'AI-datacenter-power' event test since that theme doesn't have one clean, verifiable catalyst date — this is the more rigorously anchored version of 'is XLU moving for a distinct reason.')

Treated: GOOGL. Donors: MSFT, META, AMZN (fellow Mag-7 hyperscalers, all running their own large AI-capex programs and facing similar market scrutiny over it) — hand-picked, not algorithmic. Treatment date is GOOGL's Jul 22 after-close report: Cloud grew 63% YoY, a strong number, but $190B capex guidance crushed the FCF margin (21%→10, from 21%→9.2% per prior notes) and the stock sold off anyway. Tests whether this was a GOOGL-specific reaction to its own guidance, or part of a shared 'market re-pricing hyperscaler AI-capex risk' move that should also show up in the donor pool.

Follow-up to tsla_earnings_miss_auto_peers, which failed the fit-quality gate — pure Detroit automakers (F, GM) don't move with TSLA day-to-day at all. Treated: TSLA. Donors: RIVN, LCID (EV-specific, still auto but more growth-multiple-priced than F/GM), PLTR, COIN (retail-favorite high-beta momentum names with no automotive exposure, included on the theory that TSLA trades more on 'high-beta retail-momentum growth' factor exposure than on 'automaker' exposure most days). Same Jul 22 earnings-miss treatment date. Result: improved (relative RMSE 1.29x, down from 1.78x with pure automakers) but still fails the fit-quality gate — TSLA doesn't reconstruct cleanly from either peer group tried so far. Read as: TSLA is genuinely idiosyncratic (Musk/narrative -driven) rather than a donor-pool-selection failure specifically — a case where the honest answer is 'this method can't cleanly isolate an effect here,' not a forced conclusion either way.

Treated: TSLA. Donors: F, GM, RIVN (traditional + EV auto peers) — hand-picked, not algorithmic. Treatment date is the Jul 22 earnings report (auto gross margins 16.3% vs 18.4% expected, EPS $0.33 vs $0.55 consensus, FCF went negative on AI/robotics capex). Tests whether the post-earnings selloff is a real, company-specific fundamentals break from the auto sector, or whether TSLA was already diverging from these peers beforehand for unrelated reasons. Result: fails the fit-quality gate — see tsla_earnings_miss_growth_peers for the follow-up with a different donor pool.

Treated: BE. Donors: FCEL, PLUG (fuel-cell/clean-hydrogen peers) — a hand-picked peer group, not an algorithmic match. Treatment date is the Hunterbrook Capital report (2026-07-08). Tests whether BE's decline is company-specific (a real post-treatment gap vs. peers) or just riding the same clean-energy/rate-sensitive-growth wave FCEL and PLUG were already on. Pre-period deliberately short (~4 months, not 2 years): BE's own multi-year rally is so much larger than FCEL/PLUG's that a long window makes the donor pool unusable (see README) — a short window centered on BE's recent behavior is the standard fix when the treated unit has a strong idiosyncratic long-run trend.

Validation case — reproduces the original finding from ../causal/run_causal_report.py. Confirms the generalized toolkit recovers the same (honest, negative) result: XLK's gap vs. its synthetic counterfactual opened up months before the June 17 FOMC meeting, so the pivot itself can't cleanly take credit for it.