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
Please find the software documentation here: https://amazon-science.github.io/azcausal/latest/
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.
To install the current release, please execute:
pip install git+https://github.com/amazon-science/azcausal.gitfrom 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 |
╰──────────────────────────────────────────────────────────────────────────────╯
- 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/
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.