Python package for causal inference using Bayesian structural time-series models.

Last update: Dec 11, 2022

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Overview

Python Causal Impact

Causal inference using Bayesian structural time-series models. This package aims at defining a python equivalent of the R CausalImpact package by Google. Please refer to the package itself, its documentation or the related publication (Brodersen et al., Annals of Applied Statistics, 2015) for more information.

Setup

Simply install from pip:

pip install causal-impact

Example

Suppose we have a DataFrame data recording daily measures for three different markets y, x1 and x2, for t = 0..365). The y time series in data is the one we will be modeling, while other columns (x1 and x2 here) will be used as a set of control time series.

>>> data
      y       x1      x2
  0   1735.01 1014.44 1005.87
  1   1709.54 1012.63 1008.18
  2   1772.95 1039.04 1024.21
...   ...     ...     ...

At t = date_inter = 280, a marketing campaing (the intervention) is run for market y. We want to understand the impact of that campaign on our measure.

from causal_impact import CausalImpact

ci = CausalImpact(data, date_inter, n_seasons=7)
ci.run(max_iter=1000)
ci.plot()

After fitting the model, and estimating what the y time series would have been without any intervention, this will typically produce the following plots:

If you need access to the data behind the plots for further analysis, you can simply use the ci.result attribute (pandas.DataFrame object). Alternatively, you can also call

result = ci.run(return_df=True)

and skip the plotting step.

Issues and improvements

This package is still being developed. Feel free to contribute through github by sending pull requests or reporting issues.

Comments

"unhashable type: 'slice'" error when running ci.plot()

I just downloaded Causal Impact from github and was attempting a test run with the data and code below on python 3.6.3.

I'm getting an "unhashable type: 'slice'" error when attempting the ci.plot() command, though ci.plot_components() works without issue.

Error seems to be coming from line 118 in causal_impact.py in plot(self)

Not sure if anyone else has run into this and/or has a work around, but figured I'd post since I haven't seem much documentation on this online.

========Code============ Source: https://github.com/tcassou/causal_impact/issues/5).

from causal_impact.causal_impact import CausalImpact import pandas as pd import sys from io import StringIO

DATA = """ t,y,x1,x2\n 2016-02-20 22:41:20,110.0,134.0,128.0\n 2016-02-20 22:41:30,125.0,134.0,128.0\n 2016-02-20 22:41:40,123.0,134.0,128.0\n 2016-02-20 22:41:50,128.0,134.0,128.0\n 2016-02-20 22:42:00,114.0,134.0,128.0\n 2016-02-20 22:42:10,125.0,133.0,128.0\n 2016-02-20 22:42:20,119.0,133.0,128.0\n 2016-02-20 22:42:30,121.0,133.0,128.0\n 2016-02-20 22:42:40,139.0,133.0,128.0\n 2016-02-20 22:42:50,107.0,133.0,128.0\n 2016-02-20 22:43:00,115.0,132.0,128.0\n 2016-02-20 22:43:10,91.0,132.0,128.0\n 2016-02-20 22:43:20,107.0,132.0,128.0\n 2016-02-20 22:43:30,124.0,132.0,128.0\n 2016-02-20 22:43:40,116.0,131.0,128.0\n 2016-02-20 22:43:50,110.0,131.0,128.0\n 2016-02-20 22:44:00,100.0,131.0,128.0\n 2016-02-20 22:44:10,110.0,131.0,128.0\n 2016-02-20 22:44:20,113.0,129.0,128.0\n 2016-02-20 22:44:30,103.0,129.0,128.0\n 2016-02-20 22:44:40,117.0,129.0,128.0\n 2016-02-20 22:44:50,125.0,129.0,128.0\n 2016-02-20 22:45:00,115.0,129.0,128.0\n 2016-02-20 22:45:10,114.0,128.0,128.0\n 2016-02-20 22:45:20,138.0,128.0,128.0\n 2016-02-20 22:45:30,117.0,128.0,128.0\n 2016-02-20 22:45:40,104.0,128.0,128.0\n 2016-02-20 22:45:50,123.0,128.0,128.0\n 2016-02-20 22:46:00,122.0,128.0,128.0\n 2016-02-20 22:46:10,150.0,128.0,128.0\n 2016-02-20 22:46:20,127.0,128.0,128.0\n 2016-02-20 22:46:30,139.0,128.0,128.0\n 2016-02-20 22:46:40,139.0,127.0,127.0\n 2016-02-20 22:46:50,109.0,127.0,127.0\n 2016-02-20 22:47:00,107.0,127.0,127.0\n 2016-02-20 22:47:10,94.0,127.0,127.0\n 2016-02-20 22:47:20,112.0,127.0,127.0\n 2016-02-20 22:47:30,107.0,127.0,127.0\n 2016-02-20 22:47:40,126.0,127.0,127.0\n 2016-02-20 22:47:50,114.0,127.0,127.0\n 2016-02-20 22:48:00,129.0,127.0,127.0\n 2016-02-20 22:48:10,113.0,126.0,127.0\n 2016-02-20 22:48:20,114.0,126.0,127.0\n 2016-02-20 22:48:30,116.0,126.0,127.0\n 2016-02-20 22:48:40,110.0,125.0,126.0\n 2016-02-20 22:48:50,131.0,125.0,126.0\n 2016-02-20 22:49:00,109.0,125.0,126.0\n 2016-02-20 22:49:10,114.0,125.0,127.0\n 2016-02-20 22:49:20,116.0,125.0,126.0\n 2016-02-20 22:49:30,113.0,124.0,125.0\n 2016-02-20 22:49:40,108.0,124.0,125.0\n 2016-02-20 22:49:50,120.0,124.0,125.0\n 2016-02-20 22:50:00,106.0,123.0,125.0\n 2016-02-20 22:50:10,123.0,123.0,125.0\n 2016-02-20 22:50:20,123.0,123.0,124.0\n 2016-02-20 22:50:30,135.0,123.0,124.0\n 2016-02-20 22:50:40,127.0,123.0,124.0\n 2016-02-20 22:50:50,140.0,123.0,123.0\n 2016-02-20 22:51:00,139.0,123.0,123.0\n 2016-02-20 22:51:10,137.0,123.0,123.0\n 2016-02-20 22:51:20,123.0,123.0,123.0\n 2016-02-20 22:51:30,160.0,122.0,123.0\n 2016-02-20 22:51:40,173.0,122.0,123.0\n 2016-02-20 22:51:50,236.0,122.0,123.0\n 2016-02-20 22:52:00,233.0,122.0,123.0\n 2016-02-20 22:52:10,193.0,122.0,123.0\n 2016-02-20 22:52:20,169.0,122.0,123.0\n 2016-02-20 22:52:30,167.0,122.0,123.0\n 2016-02-20 22:52:40,172.0,121.0,123.0\n 2016-02-20 22:52:50,148.0,121.0,123.0\n 2016-02-20 22:53:00,125.0,121.0,123.0\n 2016-02-20 22:53:10,132.0,121.0,123.0\n 2016-02-20 22:53:20,165.0,121.0,123.0\n 2016-02-20 22:53:30,154.0,120.0,123.0\n 2016-02-20 22:53:40,158.0,120.0,123.0\n 2016-02-20 22:53:50,135.0,120.0,123.0\n 2016-02-20 22:54:00,145.0,120.0,123.0\n 2016-02-20 22:54:10,163.0,119.0,122.0\n 2016-02-20 22:54:20,146.0,119.0,122.0\n 2016-02-20 22:54:30,120.0,119.0,121.0\n 2016-02-20 22:54:40,149.0,118.0,121.0\n 2016-02-20 22:54:50,140.0,118.0,121.0\n 2016-02-20 22:55:00,150.0,117.0,121.0\n 2016-02-20 22:55:10,133.0,117.0,120.0\n 2016-02-20 22:55:20,143.0,117.0,120.0\n 2016-02-20 22:55:30,145.0,117.0,120.0\n 2016-02-20 22:55:40,145.0,117.0,120.0\n 2016-02-20 22:55:50,176.0,117.0,120.0\n 2016-02-20 22:56:00,134.0,117.0,120.0\n 2016-02-20 22:56:10,147.0,117.0,120.0\n 2016-02-20 22:56:20,131.0,117.0,120.0"""

df = pd.read_csv(StringIO(DATA)) df["t"] = pd.to_datetime(df["t"]) df.index = df["t"] del df["t"]

ci = CausalImpact(df, pd.to_datetime('2016-02-20 22:51:20')) ci.run() ci.plot()

opened by zburkert 8

Confidence intervals differ from R CausalImpact package

Hi Thomas, Thanks for working on this! I just wanted to flag that your package doesn't produce results comparable to the R CausalImpact package yet for the same reason that @bange83 identified in https://github.com/jamalsenouci/causalimpact/issues/7

To reproduce:

from causal_impact.causal_impact import CausalImpact
import pandas as pd
import sys
from io import StringIO

DATA = """
t,y,x1,x2\n
2016-02-20 22:41:20,110.0,134.0,128.0\n
2016-02-20 22:41:30,125.0,134.0,128.0\n
2016-02-20 22:41:40,123.0,134.0,128.0\n
2016-02-20 22:41:50,128.0,134.0,128.0\n
2016-02-20 22:42:00,114.0,134.0,128.0\n
2016-02-20 22:42:10,125.0,133.0,128.0\n
2016-02-20 22:42:20,119.0,133.0,128.0\n
2016-02-20 22:42:30,121.0,133.0,128.0\n
2016-02-20 22:42:40,139.0,133.0,128.0\n
2016-02-20 22:42:50,107.0,133.0,128.0\n
2016-02-20 22:43:00,115.0,132.0,128.0\n
2016-02-20 22:43:10,91.0,132.0,128.0\n
2016-02-20 22:43:20,107.0,132.0,128.0\n
2016-02-20 22:43:30,124.0,132.0,128.0\n
2016-02-20 22:43:40,116.0,131.0,128.0\n
2016-02-20 22:43:50,110.0,131.0,128.0\n
2016-02-20 22:44:00,100.0,131.0,128.0\n
2016-02-20 22:44:10,110.0,131.0,128.0\n
2016-02-20 22:44:20,113.0,129.0,128.0\n
2016-02-20 22:44:30,103.0,129.0,128.0\n
2016-02-20 22:44:40,117.0,129.0,128.0\n
2016-02-20 22:44:50,125.0,129.0,128.0\n
2016-02-20 22:45:00,115.0,129.0,128.0\n
2016-02-20 22:45:10,114.0,128.0,128.0\n
2016-02-20 22:45:20,138.0,128.0,128.0\n
2016-02-20 22:45:30,117.0,128.0,128.0\n
2016-02-20 22:45:40,104.0,128.0,128.0\n
2016-02-20 22:45:50,123.0,128.0,128.0\n
2016-02-20 22:46:00,122.0,128.0,128.0\n
2016-02-20 22:46:10,150.0,128.0,128.0\n
2016-02-20 22:46:20,127.0,128.0,128.0\n
2016-02-20 22:46:30,139.0,128.0,128.0\n
2016-02-20 22:46:40,139.0,127.0,127.0\n
2016-02-20 22:46:50,109.0,127.0,127.0\n
2016-02-20 22:47:00,107.0,127.0,127.0\n
2016-02-20 22:47:10,94.0,127.0,127.0\n
2016-02-20 22:47:20,112.0,127.0,127.0\n
2016-02-20 22:47:30,107.0,127.0,127.0\n
2016-02-20 22:47:40,126.0,127.0,127.0\n
2016-02-20 22:47:50,114.0,127.0,127.0\n
2016-02-20 22:48:00,129.0,127.0,127.0\n
2016-02-20 22:48:10,113.0,126.0,127.0\n
2016-02-20 22:48:20,114.0,126.0,127.0\n
2016-02-20 22:48:30,116.0,126.0,127.0\n
2016-02-20 22:48:40,110.0,125.0,126.0\n
2016-02-20 22:48:50,131.0,125.0,126.0\n
2016-02-20 22:49:00,109.0,125.0,126.0\n
2016-02-20 22:49:10,114.0,125.0,127.0\n
2016-02-20 22:49:20,116.0,125.0,126.0\n
2016-02-20 22:49:30,113.0,124.0,125.0\n
2016-02-20 22:49:40,108.0,124.0,125.0\n
2016-02-20 22:49:50,120.0,124.0,125.0\n
2016-02-20 22:50:00,106.0,123.0,125.0\n
2016-02-20 22:50:10,123.0,123.0,125.0\n
2016-02-20 22:50:20,123.0,123.0,124.0\n
2016-02-20 22:50:30,135.0,123.0,124.0\n
2016-02-20 22:50:40,127.0,123.0,124.0\n
2016-02-20 22:50:50,140.0,123.0,123.0\n
2016-02-20 22:51:00,139.0,123.0,123.0\n
2016-02-20 22:51:10,137.0,123.0,123.0\n
2016-02-20 22:51:20,123.0,123.0,123.0\n
2016-02-20 22:51:30,160.0,122.0,123.0\n
2016-02-20 22:51:40,173.0,122.0,123.0\n
2016-02-20 22:51:50,236.0,122.0,123.0\n
2016-02-20 22:52:00,233.0,122.0,123.0\n
2016-02-20 22:52:10,193.0,122.0,123.0\n
2016-02-20 22:52:20,169.0,122.0,123.0\n
2016-02-20 22:52:30,167.0,122.0,123.0\n
2016-02-20 22:52:40,172.0,121.0,123.0\n
2016-02-20 22:52:50,148.0,121.0,123.0\n
2016-02-20 22:53:00,125.0,121.0,123.0\n
2016-02-20 22:53:10,132.0,121.0,123.0\n
2016-02-20 22:53:20,165.0,121.0,123.0\n
2016-02-20 22:53:30,154.0,120.0,123.0\n
2016-02-20 22:53:40,158.0,120.0,123.0\n
2016-02-20 22:53:50,135.0,120.0,123.0\n
2016-02-20 22:54:00,145.0,120.0,123.0\n
2016-02-20 22:54:10,163.0,119.0,122.0\n
2016-02-20 22:54:20,146.0,119.0,122.0\n
2016-02-20 22:54:30,120.0,119.0,121.0\n
2016-02-20 22:54:40,149.0,118.0,121.0\n
2016-02-20 22:54:50,140.0,118.0,121.0\n
2016-02-20 22:55:00,150.0,117.0,121.0\n
2016-02-20 22:55:10,133.0,117.0,120.0\n
2016-02-20 22:55:20,143.0,117.0,120.0\n
2016-02-20 22:55:30,145.0,117.0,120.0\n
2016-02-20 22:55:40,145.0,117.0,120.0\n
2016-02-20 22:55:50,176.0,117.0,120.0\n
2016-02-20 22:56:00,134.0,117.0,120.0\n
2016-02-20 22:56:10,147.0,117.0,120.0\n
2016-02-20 22:56:20,131.0,117.0,120.0"""

df = pd.read_csv(StringIO(DATA))
df["t"] = pd.to_datetime(df["t"])
df.index = df["t"]
del df["t"]

ci = CausalImpact(df, pd.to_datetime('2016-02-20 22:51:20'))
ci.run()
ci.plot()

Compare to the R CausalImpact result (see referenced issue for code):

opened by sleitner 8

Exception raised in plot. Row-Labels not set

I followed your doc and ran into this

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-68-af50b5359315> in <module>()
      3 ci = CausalImpact(causal2, date_interv)
      4 ci.run()
----> 5 ci.plot()

python3.5/site-packages/causal_impact/causal_impact.py in plot(self)
    110         pred = self.fit.get_prediction()
    111         pre_model = pred.predicted_mean
--> 112         pre_lower = pred.conf_int()['lower y'].values
    113         pre_upper = pred.conf_int()['upper y'].values
    114         pre_model[:min_t] = np.nan

IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

as far I could understand by looking inside the statsmodels code, the error is due to pred.conf_int() returning an array instead of a data frame, and that happens because pred.row_labels is None.

Reproducible code:

df = pandas.DataFrame(
    {'y': [150, 200, 225, 150, 175],
     'x1': [150, 249, 150, 125, 325],
     'x2': [275, 125, 249, 275, 250]
    }
)
date_interv = 2
ci = CausalImpact(df, date_interv)
ci.run()
ci.plot()

opened by pgrandinetti 7

Result of the python package is different than the R-package result

Dear tcassou,

Thanks for translating the R-package to this nice python-package. I have run a random dataset consisting of 71 x 2 values in both the python package and R package, but it seems that the outcomes of the predictions in both packages are different. Did you every encountered such a thing previously? And if you have any idea what might be the reason for this behaviour?

Thanks in advance.

opened by NimaRou 4
Feature Request: small multiples option for top row

Hi @tcassou. Thanks for taking the time to port causal impact over to Python. One aspect I like is that unlike the R version OOTB, you are plotting all of the time series. It got me thinking that for certain time series where the lines could be on top of each other a lot, an option to plot each time series in a small chart to view the trends side by side. I'd like to submit a pull request of this functionality. See example image here
enhancement

opened by timandrews335 3
using datetime indexes

As a user of causal-impact who is unfamiliar with reading code as documentation, is there a way to demonstrate how to run the library using a pandas data frame with a DateTime index?

opened by iampatgrady 1
Pull request for feature request #8

Pull request for feature request #8. Optional parameter in the plot function to render the top row with small time series charts next to each other, to avoid lines running into each other.

opened by timandrews335 0
Different confident interval for cummulative effect

Hi, i used this libruary for analyse and compare results with original libruary with R language. i found that confidence intervals (especially in cummulative sum) strong differ compared with original library. Could you please explain why?

opened by dmitrkozlovsk 0
ValueError: zero-size array to reduction operation maximum which has no identity

Error with simple dataset:

ValueError Traceback (most recent call last) in 1 from causal_impact import CausalImpact 2 ci = CausalImpact(test, 120, n_seasons=7) ----> 3 ci.run(max_iter=1000) 4 ci.plot()

~/anaconda3/envs/python3/lib/python3.6/site-packages/causal_impact/causal_impact.py in run(self, max_iter, return_df) 69 exog=self.data.loc[:self._inter_index - 1, self._reg_cols()].values, 70 level='local linear trend', ---> 71 seasonal=self.n_seasons, 72 ) 73 self._fit = self._model.fit(maxiter=max_iter)

~/anaconda3/envs/python3/lib/python3.6/site-packages/statsmodels/tsa/statespace/structural.py in init(self, endog, level, trend, seasonal, freq_seasonal, cycle, autoregressive, exog, irregular, stochastic_level, stochastic_trend, stochastic_seasonal, stochastic_freq_seasonal, stochastic_cycle, damped_cycle, cycle_period_bounds, mle_regression, use_exact_diffuse, **kwargs) 571 # Setup the representation 572 super(UnobservedComponents, self).init( --> 573 endog, k_states, k_posdef=k_posdef, exog=exog, **kwargs 574 ) 575 self.setup()

~/anaconda3/envs/python3/lib/python3.6/site-packages/statsmodels/tsa/statespace/mlemodel.py in init(self, endog, k_states, exog, dates, freq, **kwargs) 136 super(MLEModel, self).init(endog=endog, exog=exog, 137 dates=dates, freq=freq, --> 138 missing='none') 139 140 # Store kwargs to recreate model

~/anaconda3/envs/python3/lib/python3.6/site-packages/statsmodels/tsa/base/tsa_model.py in init(self, endog, exog, dates, freq, missing, **kwargs) 45 missing='none', **kwargs): 46 super(TimeSeriesModel, self).init(endog, exog, missing=missing, ---> 47 **kwargs) 48 49 # Date handling in indexes

~/anaconda3/envs/python3/lib/python3.6/site-packages/statsmodels/base/model.py in init(self, endog, exog, **kwargs) 234 235 def init(self, endog, exog=None, **kwargs): --> 236 super(LikelihoodModel, self).init(endog, exog, **kwargs) 237 self.initialize() 238

~/anaconda3/envs/python3/lib/python3.6/site-packages/statsmodels/base/model.py in init(self, endog, exog, **kwargs) 75 hasconst = kwargs.pop('hasconst', None) 76 self.data = self._handle_data(endog, exog, missing, hasconst, ---> 77 **kwargs) 78 self.k_constant = self.data.k_constant 79 self.exog = self.data.exog

~/anaconda3/envs/python3/lib/python3.6/site-packages/statsmodels/base/model.py in _handle_data(self, endog, exog, missing, hasconst, **kwargs) 98 99 def _handle_data(self, endog, exog, missing, hasconst, **kwargs): --> 100 data = handle_data(endog, exog, missing, hasconst, **kwargs) 101 # kwargs arrays could have changed, easier to just attach here 102 for key in kwargs:

~/anaconda3/envs/python3/lib/python3.6/site-packages/statsmodels/base/data.py in handle_data(endog, exog, missing, hasconst, **kwargs) 670 klass = handle_data_class_factory(endog, exog) 671 return klass(endog, exog=exog, missing=missing, hasconst=hasconst, --> 672 **kwargs)

~/anaconda3/envs/python3/lib/python3.6/site-packages/statsmodels/base/data.py in init(self, endog, exog, missing, hasconst, **kwargs) 85 self.const_idx = None 86 self.k_constant = 0 ---> 87 self._handle_constant(hasconst) 88 self._check_integrity() 89 self._cache = {}

~/anaconda3/envs/python3/lib/python3.6/site-packages/statsmodels/base/data.py in _handle_constant(self, hasconst) 175 (np.ones(self.exog.shape[0]), self.exog)) 176 rank_augm = np.linalg.matrix_rank(augmented_exog) --> 177 rank_orig = np.linalg.matrix_rank(self.exog) 178 self.k_constant = int(rank_orig == rank_augm) 179 self.const_idx = None

<array_function internals> in matrix_rank(*args, **kwargs)

~/anaconda3/envs/python3/lib/python3.6/site-packages/numpy/linalg/linalg.py in matrix_rank(M, tol, hermitian) 1902 S = svd(M, compute_uv=False, hermitian=hermitian) 1903 if tol is None: -> 1904 tol = S.max(axis=-1, keepdims=True) * max(M.shape[-2:]) * finfo(S.dtype).eps 1905 else: 1906 tol = asarray(tol)[..., newaxis]

~/anaconda3/envs/python3/lib/python3.6/site-packages/numpy/core/_methods.py in _amax(a, axis, out, keepdims, initial, where) 37 def _amax(a, axis=None, out=None, keepdims=False, 38 initial=_NoValue, where=True): ---> 39 return umr_maximum(a, axis, None, out, keepdims, initial, where) 40 41 def _amin(a, axis=None, out=None, keepdims=False,

ValueError: zero-size array to reduction operation maximum which has no identity

opened by sn3fru 0
Any plans to enhance with Market Matching package

Hi, sorry for dropping this under the issue section. I was wondering, if there is anything in the pipeline to extend this library with the Market Matching package the R equivalent seems to offer, to allow for dynamic time warping. reference.: e.x.: https://github.com/klarsen1/MarketMatching

thanks upfront

opened by mrkobk 0

Owner

Thomas Cassou

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A Python package for time series classification

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A data preprocessing package for time series data. Design for machine learning and deep learning.

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AtsPy: Automated Time Series Models in Python (by @firmai)

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Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

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Fourier-Bayesian estimation of stochastic volatility models

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Open source time series library for Python

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A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

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STUMPY is a powerful and scalable Python library for computing a Matrix Profile, which can be used for a variety of time series data mining tasks

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Python package for causal inference using Bayesian structural time-series models.

Related tags

Overview

Python Causal Impact

Setup

Example

Issues and improvements

Comments

"unhashable type: 'slice'" error when running ci.plot()

Confidence intervals differ from R CausalImpact package

Exception raised in plot. Row-Labels not set

Result of the python package is different than the R-package result

Feature Request: small multiples option for top row

using datetime indexes

Pull request for feature request #8

Different confident interval for cummulative effect

ValueError: zero-size array to reduction operation maximum which has no identity

Any plans to enhance with Market Matching package