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casual_inference
The casual_inference
is a Python package provides a simple interface to do causal inference.
Doing causal analyses is a complicated stuff. We have to pay attention to many things to do such analyses properly.
The casual_inference
is developed aiming to reduce such effort.
Installation
pip install casual-inference
Overview
This package will provide several types of evaluator
. They have evaluate()
and some summary_xxx()
methods. The evaluate()
method evaluates treatment impact by calculating several statistics in it, and the summary_xxx()
methods summarize such statistics in some ways. (e.g., table style, bar chart style, ...)
The evaluate()
method expects that the data which has a schema like as follows will be passed.
unit | variant | metric_A | metric_B | ... |
---|---|---|---|---|
1 | 1 | 0 | 0.01 | |
2 | 1 | 1 | 0.05 | |
3 | 2 | 0 | 0.02 | |
... | ... | ... | ... |
The table has been already aggregated by the unit
column. (i.e. The unit
column will be the priary key)
Columns
unit
: The unit you want to conduct analysis on. Typically it will be user_id, session_id, ... in the web service domain.variant
: The group of intervention. This package always assumes1
is a variant of control group.metrics
: metrics you want to evaluate. e.g., The number of purchases, conversion rate, ...
Quick Start
The current version of casual_inference
only supports evaluation of A/B testing.
A/B test evaluation
from casual_inference.dataset import create_sample_ab_result
from casual_inference.evaluator import ABTestEvaluator
data = create_sample_ab_result(n_variant=3, sample_size=1000000, simulated_lift=[-0.01, 0.01])
evaluator = ABTestEvaluator()
evaluator.evaluate(
data=data,
unit_col="rand_unit",
variant_col="variant",
metrics=["metric_bin", "metric_cont"]
)
evaluator.summary_barplot()
You can also see the example notebook to see more detailed example.
References
- Kohavi, Ron, Diane Tang, and Ya Xu. 2020. Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. https://experimentguide.com/
- A Great book covering comprehensive topics around practical A/B testing. I do recommend to read this book for all people who works on A/B testing.
- Alex Deng, Ulf Knoblich, and Jiannan Lu. 2018. Applying the Delta Method in Metric Analytics: A Practical Guide with Novel Ideas. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). Association for Computing Machinery, New York, NY, USA, 233–242. https://doi.org/10.1145/3219819.3219919
- Describing how to approximate variance of relative difference, and when the analysis unit was more granular than the randomization unit.
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