diff_diff.to_pymc_marketing_lift_test#

diff_diff.to_pymc_marketing_lift_test(*, channel, x, delta_x, delta_y, sigma, dims=None, on_wrong_sign='raise')[source]

Assemble a PyMC-Marketing lift-test DataFrame from scoped experiment results.

Produces one row per experiment with columns channel, any dims columns, x, delta_x, delta_y, sigma - the exact schema consumed by pymc_marketing.mmm.MMM.add_lift_test_measurements (prophetverse’s lift-test API accepts the same shape). All values stay in original data units (PyMC-Marketing rescales internally).

The caller supplies the scoped effect. delta_y and sigma are the measured incremental outcome and its standard error, already aggregated to the population and time window ONE target-MMM row represents. This function does not rescale an ATT, because the reconciliation needs the MMM’s row granularity and outcome scale, which it cannot see. Read the effect and SE off your fitted result’s summary() and scope them yourself: PyMC-Marketing scores one row’s delta_y against saturation(x + delta_x) - saturation(x), so x, delta_x, delta_y, and sigma must all describe the SAME observation (same channel, same population, same period span, additive-level outcome).

Parameters:

  • channel (str or sequence of str) – MMM channel name(s); must match the target model’s channel_columns.

  • x (float or sequence of float) – Baseline channel spend for the row’s observation, in original spend units.

  • delta_x (float or sequence of float) – Spend change during the experiment (nonzero; negative for go-dark/holdout tests, with x + delta_x >= 0), in the same units as x.

  • delta_y (float or sequence of float) – Measured incremental outcome for the SAME observation as x/delta_x, in original outcome units. Finite; must be nonzero and share delta_x’s sign (see on_wrong_sign).

  • sigma (float or sequence of float) – Standard error of delta_y (finite, positive).

  • dims (mapping or sequence of mappings, optional) – Extra model-dimension columns, e.g. {"geo": "US-CA"} for a geo-level MMM built with MMM(dims=("geo",)). Values must match the target model’s coordinate values exactly and identify the population delta_y describes (do not label a multi-geo average with one specific geo). All rows must share one key set; column order follows the first mapping.

  • on_wrong_sign ({"raise", "drop", "keep"}, default "raise") – Policy for rows PyMC-Marketing cannot use: sign(delta_y) contradicting sign(delta_x) (rejected upstream with NonMonotonicError), or delta_y == 0 (degenerate for its strictly-positive Gamma lift likelihood, which its own monotonicity check does not catch). "raise" (default) errors with guidance; "drop" warns and removes such rows (raises if that would empty the frame); "keep" warns and emits them anyway (for non-PyMC consumers - the frame is not valid PyMC-Marketing input).

Returns:

Columns [channel, *dims, x, delta_x, delta_y, sigma], one row per experiment.

Return type:

pd.DataFrame

Raises:

ValueError – On invalid inputs (non-positive sigma, non-finite values, delta_x == 0, x + delta_x < 0, broadcasting length mismatches, empty inputs, heterogeneous dims key sets) or wrong-signed/zero rows under the default policy.