RegressionDiscontinuity#

class causalpy.experiments.regression_discontinuity.RegressionDiscontinuity[source]#

A class to analyse sharp regression discontinuity experiments.

Parameters:

  • data (NativeDataFrame) – Any eager dataframe Narwhals supports, such as pandas, Polars, or PyArrow. Converted to pandas internally.

  • formula (str) – A statistical model formula.

  • treatment_threshold (float) – A scalar threshold value at which the treatment is applied.

  • model (PyMCModel | RegressorMixin | None) – A PyMC or sklearn model. Defaults to LinearRegression.

  • running_variable_name (str) – The name of the predictor variable that the treatment threshold is based upon.

  • epsilon (float) – A small scalar value which determines how far above and below the treatment threshold to evaluate the causal impact.

  • bandwidth (float) – Data outside of the bandwidth (relative to the discontinuity) is not used to fit the model.

  • donut_hole (float) – Observations within this distance from the treatment threshold are excluded from model fitting. Used as a robustness check when observations closest to the threshold may be problematic (e.g., due to manipulation or heaping). Must be non-negative and less than bandwidth if bandwidth is finite.

Notes

Estimate extraction

After fitting the regression on the selected bandwidth, the class predicts the conditional expectation immediately below the threshold with treated=0 and immediately above it with treated=1. discontinuity_at_threshold is the upper prediction minus the lower prediction, evaluated at threshold ± epsilon. This is a local prediction contrast, not a population-standardized effect.

Examples

>>> import causalpy as cp
>>> df = cp.load_data("rd")
>>> seed = 42
>>> result = cp.RegressionDiscontinuity(
...     df,
...     formula="y ~ 1 + x + treated + x:treated",
...     model=cp.pymc_models.LinearRegression(
...         sample_kwargs={
...             "draws": 100,
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         },
...     ),
...     treatment_threshold=0.5,
... )

Methods

RegressionDiscontinuity.algorithm()

Run the experiment algorithm: fit model, predict, and calculate discontinuity.

RegressionDiscontinuity.effect_summary(*[, ...])

Generate a decision-ready summary of causal effects for Regression Discontinuity.

RegressionDiscontinuity.generate_report(*[, ...])

Generate a self-contained HTML report for this experiment.

RegressionDiscontinuity.input_validation()

Validate the input data and model formula for correctness.

RegressionDiscontinuity.plot(*[, round_to, ...])

Plot the regression discontinuity results.

RegressionDiscontinuity.print_coefficients([...])

Ask the model to print its coefficients.

RegressionDiscontinuity.set_maketables_options(*)

Set optional maketables rendering options for this experiment.

RegressionDiscontinuity.summary([round_to])

Print summary of main results and model coefficients.

Attributes

idata

Return fitted DataTree when the model backend supports it.

supports_bayes

supports_ols

supports_pymc_forecast

labels

data

__init__(data, formula, treatment_threshold, model=None, running_variable_name='x', epsilon=0.001, bandwidth=inf, donut_hole=0.0)[source]#
Parameters:
Return type:

None

classmethod __new__(*args, **kwargs)#