azcausal 0.2.1

Casual Inference

Causal inference is an important component of the experiment evaluation. We highly recommend to have a look at the open-source book: Causal Inference for The Brave and True

Currently, azcausal provides two well-known and widely used causal inference methods: Difference-in-Difference (DID) and Synthetic Difference-in-Difference (SDID). Moreover, error estimates via Placebo, Boostrap, or JackKnife are available.

Installation

To install the current release, please execute:

pip install git+https://github.com/amazon-science/azcausal.git

Usage

from azcausal.core.error import JackKnife
from azcausal.core.panel import CausalPanel
from azcausal.data import CaliforniaProp99
from azcausal.estimators.panel.sdid import SDID
from azcausal.util import to_panels


# load an example data set with the columns Year, State, PacksPerCapita, treated.
df = CaliforniaProp99().df()

# create the panel data from the frame and define the causal types
data = to_panels(df, 'Year', 'State', ['PacksPerCapita', 'treated'])
ctypes = dict(outcome='PacksPerCapita', time='Year', unit='State', intervention='treated')

# initialize the panel
panel = CausalPanel(data).setup(**ctypes)

# initialize an estimator object, here synthetic difference in difference (sdid)
estimator = SDID()

# run the estimator
result = estimator.fit(panel)

# run the error validation method
estimator.error(result, JackKnife())

# plot the results
estimator.plot(result)

# print out information about the estimate
print(result.summary(title="CaliforniaProp99"))

╭──────────────────────────────────────────────────────────────────────────────╮
|                               CaliforniaProp99                               |
├──────────────────────────────────────────────────────────────────────────────┤
|                                    Panel                                     |
|  Time Periods: 31 (19/12)                                  total (pre/post)  |
|  Units: 39 (38/1)                                       total (contr/treat)  |
├──────────────────────────────────────────────────────────────────────────────┤
|                                     ATT                                      |
|  Effect (±SE): -15.60 (±2.9161)                                              |
|  Confidence Interval (95%): [-21.32 , -9.8884]                          (-)  |
|  Observed: 60.35                                                             |
|  Counter Factual: 75.95                                                      |
├──────────────────────────────────────────────────────────────────────────────┤
|                                  Percentage                                  |
|  Effect (±SE): -20.54 (±3.8393)                                              |
|  Confidence Interval (95%): [-28.07 , -13.02]                           (-)  |
|  Observed: 79.46                                                             |
|  Counter Factual: 100.00                                                     |
├──────────────────────────────────────────────────────────────────────────────┤
|                                  Cumulative                                  |
|  Effect (±SE): -187.25 (±34.99)                                              |
|  Confidence Interval (95%): [-255.83 , -118.66]                         (-)  |
|  Observed: 724.20                                                            |
|  Counter Factual: 911.45                                                     |
╰──────────────────────────────────────────────────────────────────────────────╯

Estimators

• Difference-in-Difference (DID): Simple implementation of the well-known Difference-in-Difference estimator.

• Synthetic Difference-in-Difference (SDID): Arkhangelsky, Dmitry Athey, Susan Hirshberg, David A. Imbens, Guido W. Wager, Stefan Synthetic Difference-in-Differences American Economic Review 111 12 4088-4118 2021 10.1257/aer.20190159 https://www.aeaweb.org/articles?id=10.1257/aer.20190159. Implementation based on https://synth-inference.github.io/synthdid/

Contact

Feel free to contact me if you have any questions:

Julian Blank (blankjul [at] amazon.com)

Amazon.com

Applied Scientist, Amazon

410 Terry Ave N, Seattle 98109, WA.