diff_diff.simulate_mde#

diff_diff.simulate_mde(estimator, n_units=100, n_periods=4, treatment_fraction=0.5, treatment_period=2, sigma=1.0, n_simulations=200, power=0.8, alpha=0.05, effect_range=None, tol=0.02, max_steps=15, seed=None, data_generator=None, data_generator_kwargs=None, estimator_kwargs=None, result_extractor=None, progress=True, survey_config=None)[source]

Find the minimum detectable effect via simulation-based bisection search.

Searches over effect sizes to find the smallest effect that achieves the target power, using simulate_power() at each step.

Parameters:

  • estimator (estimator object) – DiD estimator to use.

  • n_units (int, default=100) – Number of units per simulation.

  • n_periods (int, default=4) – Number of time periods.

  • treatment_fraction (float, default=0.5) – Fraction of units that are treated.

  • treatment_period (int, default=2) – First post-treatment period (0-indexed).

  • sigma (float, default=1.0) – Residual standard deviation.

  • n_simulations (int, default=200) – Simulations per bisection step.

  • power (float, default=0.80) – Target power.

  • alpha (float, default=0.05) – Significance level.

  • effect_range (tuple of (float, float), optional) – (lo, hi) bracket for the search. If None, auto-brackets.

  • tol (float, default=0.02) – Convergence tolerance on power.

  • max_steps (int, default=15) – Maximum bisection steps.

  • seed (int, optional) – Random seed for reproducibility.

  • data_generator (callable, optional) – Custom data generation function.

  • data_generator_kwargs (dict, optional) – Additional keyword arguments for data generator.

  • estimator_kwargs (dict, optional) – Additional keyword arguments for estimator.fit().

  • result_extractor (callable, optional) – Custom function to extract results from the estimator output. Forwarded to simulate_power().

  • progress (bool, default=True) – Whether to print progress updates.

  • survey_config (SurveyPowerConfig, optional) – Survey-aware simulation config. Forwarded to simulate_power(). See simulate_power() for details and constraints.

Returns:

Results including the MDE and search diagnostics.

Return type:

SimulationMDEResults

Examples

>>> from diff_diff import simulate_mde, DifferenceInDifferences
>>> result = simulate_mde(DifferenceInDifferences(), n_simulations=100, seed=42)
>>> print(f"MDE: {result.mde:.3f}")