Causal inference · Experiments

Lift you can defend.

Run geo lift tests when you can't randomise users. Run A/B properly when you can. Send the result back into the next MMM run as a Bayesian prior — one click. Same tooling marketing-science teams build by hand, productised.

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GeoLift / augmented SCM✦PyMC Bayesian A/B✦CUPED variance reduction✦EconML HTE (advanced)

What's in the box

From design to readout, in the same workspace.

Geo experiment design

Pick treatment and control regions on an interactive UK / US map. Power analysis, MDE estimation, donor-pool diagnostics — before you spend a penny.

Augmented synthetic control

GeoLift under the hood: augmented SCM (Ben-Michael, Feller & Rothstein, 2021) with placebo diagnostics, pre-period fit reporting, and synthetic-DiD as an optional method.

A/B analysis

Frequentist baseline (statsmodels), Bayesian alternative (PyMC), CUPED variance reduction, multiple-comparison guardrails. Show calibration, not just p-values.

Heterogeneous effects

Causal Forests (Wager & Athey, 2018) and Double / Debiased ML (Chernozhukov et al., 2018) via EconML. Gated as advanced — easy to misinterpret, surfaced with caveats.

Send to MMM as prior

Click. The lift estimate attaches as a Bayesian prior on the relevant channel coefficient in the next MMM run. The MMM treats it as a calibration target with the credible interval.

Pre-registration

Lock the analysis plan before the test runs. Reduces the temptation to p-hack — and gives the readout credibility marketing-science teams trust.

Methodology

The canonical references.

Synthetic Control

Abadie, Diamond & Hainmueller — 2003 / 2010 / 2015

Foundational method. Understand the failure modes before you ship a result.

Augmented Synthetic Control

Ben-Michael, Feller & Rothstein — 2021

Robustness improvement on classical SCM. What GeoLift uses internally.

CausalImpact

Brodersen et al. (Google) — 2015

Bayesian structural time series. Available as a cross-check method.

CUPED variance reduction

Deng, Xu, Kohavi & Walker (Microsoft) — 2013

Massively reduces required sample sizes for unit-level A/B.

Causal Forests / GRF

Wager & Athey — 2018

Heterogeneous treatment effect estimation with CIs. Advanced mode only.

FAQ

Experiments, in detail.

Because most of your media spend is on broad-reach channels — TV, OOH, big-bid digital — where you can't randomise at the user level. Geo experiments use spatial variation as the randomisation. Augmented synthetic control gives you a credible counterfactual when classical pre/post DiD breaks down.

You can, but you shouldn't. Pre-registration on Kleyr Experiments locks the analysis plan before the test goes live: KPI, treatment / control, analysis window, primary statistical test. The readout flags any post-hoc additions for full transparency.

Methodology. Triple Whale runs lightweight incrementality tests built around their attribution platform. Kleyr Experiments uses GeoLift's augmented synthetic control with full diagnostics, donor-pool reporting, and pre-registration. Closer to Mutinex / Recast methodology at a mid-market price.

Available alongside the frequentist analysis. PyMC-based with non-informative or weakly informative priors by default, posterior probability of effect direction, credible intervals. The calibrated, less-misinterpretable cousin of the p-value.

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Bring your spend, leave with a model you can defend.

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