causalpy.pymc_experiments

Experiment routines for PyMC models.

  • ExperimentalDesign base class

  • Pre-Post Fit

  • Interrupted Time Series

  • Synthetic Control

  • Difference in differences

  • Regression Discontinuity

  • Pretest/Posttest Nonequivalent Group Design

class causalpy.pymc_experiments.DifferenceInDifferences

A class to analyse data from Difference in Difference settings.

Note

There is no pre/post intervention data distinction for DiD, we fit all the data available.

Parameters:
  • data (DataFrame) – A pandas dataframe

  • formula (str) – A statistical model formula

  • time_variable_name (str) – Name of the data column for the time variable

  • group_variable_name (str) – Name of the data column for the group variable

  • model – A PyMC model for difference in differences

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.pymc_experiments.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...         sample_kwargs={
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         }
...     )
...  )
__init__(data, formula, time_variable_name, group_variable_name, model=None, **kwargs)
Parameters:
  • data (DataFrame)

  • formula (str)

  • time_variable_name (str)

  • group_variable_name (str)

expt_type = None
property idata

Access to the models InferenceData object

model = None
plot(round_to=None)

Plot the results.

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

print_coefficients(round_to=None)

Prints the model coefficients

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.pymc_experiments.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...             sample_kwargs={
...                 "draws": 2000,
...                 "random_seed": seed,
...                 "progressbar": False
...             }),
...  )
>>> result.print_coefficients(round_to=1) 
Model coefficients:
Intercept                     1, 94% HDI [1, 1]
post_treatment[T.True]        1, 94% HDI [0.9, 1]
group                         0.2, 94% HDI [0.09, 0.2]
group:post_treatment[T.True]  0.5, 94% HDI [0.4, 0.6]
sigma                         0.08, 94% HDI [0.07, 0.1]
summary(round_to=None)

Print text output summarising the results.

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

class causalpy.pymc_experiments.ExperimentalDesign

Base class for other experiment types

See subclasses for examples of most methods

__init__(model=None, **kwargs)
expt_type = None
property idata

Access to the models InferenceData object

model = None
print_coefficients(round_to=None)

Prints the model coefficients

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.pymc_experiments.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...             sample_kwargs={
...                 "draws": 2000,
...                 "random_seed": seed,
...                 "progressbar": False
...             }),
...  )
>>> result.print_coefficients(round_to=1) 
Model coefficients:
Intercept                     1, 94% HDI [1, 1]
post_treatment[T.True]        1, 94% HDI [0.9, 1]
group                         0.2, 94% HDI [0.09, 0.2]
group:post_treatment[T.True]  0.5, 94% HDI [0.4, 0.6]
sigma                         0.08, 94% HDI [0.07, 0.1]
class causalpy.pymc_experiments.InstrumentalVariable

A class to analyse instrumental variable style experiments.

Parameters:
  • instruments_data (DataFrame) – A pandas dataframe of instruments for our treatment variable. Should contain instruments Z, and treatment t

  • data (DataFrame) – A pandas dataframe of covariates for fitting the focal regression of interest. Should contain covariates X including treatment t and outcome y

  • instruments_formula (str) – A statistical model formula for the instrumental stage regression e.g. t ~ 1 + z1 + z2 + z3

  • formula (str) –

    A statistical model formula for the

    focal regression e.g. y ~ 1 + t + x1 + x2 + x3

  • model – A PyMC model

  • priors

    An optional dictionary of priors for the mus and sigmas of both regressions. If priors are not specified we will substitue MLE estimates for the beta coefficients. Greater control can be achieved by specifying the priors directly e.g. priors = {

    ”mus”: [0, 0], “sigmas”: [1, 1], “eta”: 2, “lkj_sd”: 2, }

Example

>>> import pandas as pd
>>> import causalpy as cp
>>> from causalpy.pymc_experiments import InstrumentalVariable
>>> from causalpy.pymc_models import InstrumentalVariableRegression
>>> import numpy as np
>>> N = 100
>>> e1 = np.random.normal(0, 3, N)
>>> e2 = np.random.normal(0, 1, N)
>>> Z = np.random.uniform(0, 1, N)
>>> ## Ensure the endogeneity of the the treatment variable
>>> X = -1 + 4 * Z + e2 + 2 * e1
>>> y = 2 + 3 * X + 3 * e1
>>> test_data = pd.DataFrame({"y": y, "X": X, "Z": Z})
>>> sample_kwargs = {
...     "tune": 1,
...     "draws": 5,
...     "chains": 1,
...     "cores": 4,
...     "target_accept": 0.95,
...     "progressbar": False,
...     }
>>> instruments_formula = "X  ~ 1 + Z"
>>> formula = "y ~  1 + X"
>>> instruments_data = test_data[["X", "Z"]]
>>> data = test_data[["y", "X"]]
>>> iv = InstrumentalVariable(
...         instruments_data=instruments_data,
...         data=data,
...         instruments_formula=instruments_formula,
...         formula=formula,
...         model=InstrumentalVariableRegression(sample_kwargs=sample_kwargs),
... )
__init__(instruments_data, data, instruments_formula, formula, model=None, priors=None, **kwargs)
Parameters:
  • instruments_data (DataFrame)

  • data (DataFrame)

  • instruments_formula (str)

  • formula (str)

expt_type = None
get_2SLS_fit()

Two Stage Least Squares Fit

This function is called by the experiment, results are used for priors if none are provided.

get_naive_OLS_fit()

Naive Ordinary Least Squares

This function is called by the experiment.

property idata

Access to the models InferenceData object

model = None
print_coefficients(round_to=None)

Prints the model coefficients

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.pymc_experiments.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...             sample_kwargs={
...                 "draws": 2000,
...                 "random_seed": seed,
...                 "progressbar": False
...             }),
...  )
>>> result.print_coefficients(round_to=1) 
Model coefficients:
Intercept                     1, 94% HDI [1, 1]
post_treatment[T.True]        1, 94% HDI [0.9, 1]
group                         0.2, 94% HDI [0.09, 0.2]
group:post_treatment[T.True]  0.5, 94% HDI [0.4, 0.6]
sigma                         0.08, 94% HDI [0.07, 0.1]
class causalpy.pymc_experiments.InterruptedTimeSeries

A wrapper around PrePostFit class

Parameters:
  • data (DataFrame) – A pandas dataframe

  • treatment_time (Union[int, float, Timestamp]) – The time when treatment occured, should be in reference to the data index

  • formula (str) – A statistical model formula

  • model – A PyMC model

Example

>>> import causalpy as cp
>>> df = (
...     cp.load_data("its")
...     .assign(date=lambda x: pd.to_datetime(x["date"]))
...     .set_index("date")
... )
>>> treatment_time = pd.to_datetime("2017-01-01")
>>> seed = 42
>>> result = cp.pymc_experiments.InterruptedTimeSeries(
...     df,
...     treatment_time,
...     formula="y ~ 1 + t + C(month)",
...     model=cp.pymc_models.LinearRegression(
...         sample_kwargs={
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         }
...     )
... )
__init__(data, treatment_time, formula, model=None, **kwargs)
Parameters:
  • data (DataFrame)

  • treatment_time (int | float | Timestamp)

  • formula (str)

Return type:

None

expt_type = 'Interrupted Time Series'
property idata

Access to the models InferenceData object

model = None
plot(counterfactual_label='Counterfactual', round_to=None, **kwargs)

Plot the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

print_coefficients(round_to=None)

Prints the model coefficients

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.pymc_experiments.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...             sample_kwargs={
...                 "draws": 2000,
...                 "random_seed": seed,
...                 "progressbar": False
...             }),
...  )
>>> result.print_coefficients(round_to=1) 
Model coefficients:
Intercept                     1, 94% HDI [1, 1]
post_treatment[T.True]        1, 94% HDI [0.9, 1]
group                         0.2, 94% HDI [0.09, 0.2]
group:post_treatment[T.True]  0.5, 94% HDI [0.4, 0.6]
sigma                         0.08, 94% HDI [0.07, 0.1]
summary(round_to=None)

Print text output summarising the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

class causalpy.pymc_experiments.InversePropensityWeighting

A class to analyse inverse propensity weighting experiments.

Parameters:
  • data (DataFrame) – A pandas dataframe

  • formula (str) – A statistical model formula for the propensity model

:param outcome_variable

A string denoting the outcome variable in datq to be reweighted

Parameters:
  • weighting_scheme (str) – A string denoting which weighting scheme to use among: ‘raw’, ‘robust’, ‘doubly robust’ or ‘overlap’. See Aronow and Miller “Foundations of Agnostic Statistics” for discussion and computation of these weighting schemes.

  • model – A PyMC model

Example

>>> import causalpy as cp
>>> df = cp.load_data("nhefs")
>>> seed = 42
>>> result = cp.pymc_experiments.InversePropensityWeighting(
...     df,
...     formula="trt ~ 1 + age + race",
...     outcome_variable ="outcome",
...     weighting_scheme="robust",
...     model=cp.pymc_models.PropensityScore(
...         sample_kwargs={
...             "draws": 100,
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         },
...     ),
... )
__init__(data, formula, outcome_variable, weighting_scheme, model=None, **kwargs)
Parameters:
  • data (DataFrame)

  • formula (str)

  • outcome_variable (str)

  • weighting_scheme (str)

expt_type = None
get_ate(i, idata, method='doubly_robust')
property idata

Access to the models InferenceData object

make_doubly_robust_adjustment(ps)

The doubly robust weighting scheme is also discussed in Aronow and Miller, but a bit more generally than our implementation here. Here we have specified the outcome model to be a simple OLS model. In this way the compromise between the outcome model and the propensity model is always done with OLS.

make_overlap_adjustments(ps)

This weighting scheme was adapted from Lucy D’Agostino McGowan’s blog on Propensity Score Weights referenced in the primary CausalPy explainer notebook

make_raw_adjustments(ps)

This estimator is discussed in Aronow and Miller as the simplest of base form of inverse propensity weighting schemes

make_robust_adjustments(ps)

This estimator is discussed in Aronow and Miller’s book as being related to the Horvitz Thompson method

model = None
plot_ATE(idata=None, method=None, prop_draws=100, ate_draws=300)
plot_balance_ecdf(covariate, idata=None, weighting_scheme=None)

Plotting function takes a single covariate and shows the differences in the ECDF between the treatment and control groups before and after weighting. It provides a visual check on the balance achieved by using the different weighting schemes

print_coefficients(round_to=None)

Prints the model coefficients

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.pymc_experiments.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...             sample_kwargs={
...                 "draws": 2000,
...                 "random_seed": seed,
...                 "progressbar": False
...             }),
...  )
>>> result.print_coefficients(round_to=1) 
Model coefficients:
Intercept                     1, 94% HDI [1, 1]
post_treatment[T.True]        1, 94% HDI [0.9, 1]
group                         0.2, 94% HDI [0.09, 0.2]
group:post_treatment[T.True]  0.5, 94% HDI [0.4, 0.6]
sigma                         0.08, 94% HDI [0.07, 0.1]
weighted_percentile(data, weights, perc)

perc : percentile in [0-1]!

class causalpy.pymc_experiments.PrePostFit

A class to analyse quasi-experiments where parameter estimation is based on just the pre-intervention data.

Parameters:
  • data (DataFrame) – A pandas dataframe

  • treatment_time (Union[int, float, Timestamp]) – The time when treatment occured, should be in reference to the data index

  • formula (str) – A statistical model formula

  • model – A PyMC model

Example

>>> import causalpy as cp
>>> sc = cp.load_data("sc")
>>> treatment_time = 70
>>> seed = 42
>>> result = cp.pymc_experiments.PrePostFit(
...     sc,
...     treatment_time,
...     formula="actual ~ 0 + a + g",
...     model=cp.pymc_models.WeightedSumFitter(
...         sample_kwargs={
...             "draws": 400,
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False
...         }
...     ),
... )
>>> result.summary(round_to=1) 
==================================Pre-Post Fit==================================
Formula: actual ~ 0 + a + g
Model coefficients:
a                             0.6, 94% HDI [0.6, 0.6]
g                             0.4, 94% HDI [0.4, 0.4]
sigma                         0.8, 94% HDI [0.6, 0.9]
__init__(data, treatment_time, formula, model=None, **kwargs)
Parameters:
  • data (DataFrame)

  • treatment_time (int | float | Timestamp)

  • formula (str)

Return type:

None

expt_type = None
property idata

Access to the models InferenceData object

model = None
plot(counterfactual_label='Counterfactual', round_to=None, **kwargs)

Plot the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

print_coefficients(round_to=None)

Prints the model coefficients

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.pymc_experiments.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...             sample_kwargs={
...                 "draws": 2000,
...                 "random_seed": seed,
...                 "progressbar": False
...             }),
...  )
>>> result.print_coefficients(round_to=1) 
Model coefficients:
Intercept                     1, 94% HDI [1, 1]
post_treatment[T.True]        1, 94% HDI [0.9, 1]
group                         0.2, 94% HDI [0.09, 0.2]
group:post_treatment[T.True]  0.5, 94% HDI [0.4, 0.6]
sigma                         0.08, 94% HDI [0.07, 0.1]
summary(round_to=None)

Print text output summarising the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

class causalpy.pymc_experiments.PrePostNEGD

A class to analyse data from pretest/posttest designs

Parameters:
  • data (DataFrame) – A pandas dataframe

  • formula (str) – A statistical model formula

  • group_variable_name (str) – Name of the column in data for the group variable, should be either binary or boolean

  • pretreatment_variable_name (str) – Name of the column in data for the pretreatment variable

  • model – A PyMC model

Example

>>> import causalpy as cp
>>> df = cp.load_data("anova1")
>>> seed = 42
>>> result = cp.pymc_experiments.PrePostNEGD(
...     df,
...     formula="post ~ 1 + C(group) + pre",
...     group_variable_name="group",
...     pretreatment_variable_name="pre",
...     model=cp.pymc_models.LinearRegression(
...         sample_kwargs={
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         }
...     )
... )
>>> result.summary(round_to=1) 
==================Pretest/posttest Nonequivalent Group Design===================
Formula: post ~ 1 + C(group) + pre

Results:
Causal impact = 2, $CI_{94%}$[2, 2]
Model coefficients:
Intercept                     -0.5, 94% HDI [-1, 0.2]
C(group)[T.1]                 2, 94% HDI [2, 2]
pre                           1, 94% HDI [1, 1]
sigma                         0.5, 94% HDI [0.5, 0.6]
__init__(data, formula, group_variable_name, pretreatment_variable_name, model=None, **kwargs)
Parameters:
  • data (DataFrame)

  • formula (str)

  • group_variable_name (str)

  • pretreatment_variable_name (str)

expt_type = None
property idata

Access to the models InferenceData object

model = None
plot(round_to=None)

Plot the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

print_coefficients(round_to=None)

Prints the model coefficients

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.pymc_experiments.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...             sample_kwargs={
...                 "draws": 2000,
...                 "random_seed": seed,
...                 "progressbar": False
...             }),
...  )
>>> result.print_coefficients(round_to=1) 
Model coefficients:
Intercept                     1, 94% HDI [1, 1]
post_treatment[T.True]        1, 94% HDI [0.9, 1]
group                         0.2, 94% HDI [0.09, 0.2]
group:post_treatment[T.True]  0.5, 94% HDI [0.4, 0.6]
sigma                         0.08, 94% HDI [0.07, 0.1]
summary(round_to=None)

Print text output summarising the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

class causalpy.pymc_experiments.RegressionDiscontinuity

A class to analyse sharp regression discontinuity experiments.

Parameters:
  • data (DataFrame) – A pandas dataframe

  • formula (str) – A statistical model formula

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

  • model – A PyMC model

  • 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.

Example

>>> import causalpy as cp
>>> df = cp.load_data("rd")
>>> seed = 42
>>> result = cp.pymc_experiments.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,
... )
__init__(data, formula, treatment_threshold, model=None, running_variable_name='x', epsilon=0.001, bandwidth=inf, **kwargs)
Parameters:
  • data (DataFrame)

  • formula (str)

  • treatment_threshold (float)

  • running_variable_name (str)

  • epsilon (float)

  • bandwidth (float)

expt_type = None
property idata

Access to the models InferenceData object

model = None
plot(round_to=None)

Plot the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

print_coefficients(round_to=None)

Prints the model coefficients

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.pymc_experiments.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...             sample_kwargs={
...                 "draws": 2000,
...                 "random_seed": seed,
...                 "progressbar": False
...             }),
...  )
>>> result.print_coefficients(round_to=1) 
Model coefficients:
Intercept                     1, 94% HDI [1, 1]
post_treatment[T.True]        1, 94% HDI [0.9, 1]
group                         0.2, 94% HDI [0.09, 0.2]
group:post_treatment[T.True]  0.5, 94% HDI [0.4, 0.6]
sigma                         0.08, 94% HDI [0.07, 0.1]
summary(round_to=None)

Print text output summarising the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

class causalpy.pymc_experiments.RegressionKink

A class to analyse sharp regression kink experiments.

Parameters:
  • data (DataFrame) – A pandas dataframe

  • formula (str) – A statistical model formula

  • kink_point (float) – A scalar threshold value at which there is a change in the first derivative of the assignment function

  • model – A PyMC model

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

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

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

__init__(data, formula, kink_point, model=None, running_variable_name='x', epsilon=0.001, bandwidth=inf, **kwargs)
Parameters:
  • data (DataFrame)

  • formula (str)

  • kink_point (float)

  • running_variable_name (str)

  • epsilon (float)

  • bandwidth (float)

expt_type = None
property idata

Access to the models InferenceData object

model = None
plot(round_to=None)

Plot the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

print_coefficients(round_to=None)

Prints the model coefficients

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.pymc_experiments.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...             sample_kwargs={
...                 "draws": 2000,
...                 "random_seed": seed,
...                 "progressbar": False
...             }),
...  )
>>> result.print_coefficients(round_to=1) 
Model coefficients:
Intercept                     1, 94% HDI [1, 1]
post_treatment[T.True]        1, 94% HDI [0.9, 1]
group                         0.2, 94% HDI [0.09, 0.2]
group:post_treatment[T.True]  0.5, 94% HDI [0.4, 0.6]
sigma                         0.08, 94% HDI [0.07, 0.1]
summary(round_to=None)

Print text output summarising the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

class causalpy.pymc_experiments.SyntheticControl

A wrapper around the PrePostFit class

Parameters:
  • data (DataFrame) – A pandas dataframe

  • treatment_time (Union[int, float, Timestamp]) – The time when treatment occured, should be in reference to the data index

  • formula (str) – A statistical model formula

  • model – A PyMC model

Example

>>> import causalpy as cp
>>> df = cp.load_data("sc")
>>> treatment_time = 70
>>> seed = 42
>>> result = cp.pymc_experiments.SyntheticControl(
...     df,
...     treatment_time,
...     formula="actual ~ 0 + a + b + c + d + e + f + g",
...     model=cp.pymc_models.WeightedSumFitter(
...         sample_kwargs={
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         }
...     ),
... )
__init__(data, treatment_time, formula, model=None, **kwargs)
Parameters:
  • data (DataFrame)

  • treatment_time (int | float | Timestamp)

  • formula (str)

Return type:

None

expt_type = 'Synthetic Control'
property idata

Access to the models InferenceData object

model = None
plot(plot_predictors=False, **kwargs)

Plot the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

print_coefficients(round_to=None)

Prints the model coefficients

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.pymc_experiments.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...             sample_kwargs={
...                 "draws": 2000,
...                 "random_seed": seed,
...                 "progressbar": False
...             }),
...  )
>>> result.print_coefficients(round_to=1) 
Model coefficients:
Intercept                     1, 94% HDI [1, 1]
post_treatment[T.True]        1, 94% HDI [0.9, 1]
group                         0.2, 94% HDI [0.09, 0.2]
group:post_treatment[T.True]  0.5, 94% HDI [0.4, 0.6]
sigma                         0.08, 94% HDI [0.07, 0.1]
summary(round_to=None)

Print text output summarising the results

Parameters:

round_to – Number of decimals used to round results. Defaults to 2. Use “None” to return raw numbers.

Return type:

None