CausalPy
A Python package focussing on causal inference in quasi-experimental settings. The package allows for sophisticated Bayesian model fitting methods to be used in addition to traditional OLS.
Installation
To get the latest release:
pip install CausalPy
Alternatively, if you want the very latest version of the package you can install from GitHub:
pip install git+https://github.com/pymc-labs/CausalPy.git
Quickstart
import causalpy as cp
import matplotlib.pyplot as plt
# Import and process data
df = (
cp.load_data("drinking")
.rename(columns={"agecell": "age"})
.assign(treated=lambda df_: df_.age > 21)
)
# Run the analysis
result = cp.RegressionDiscontinuity(
df,
formula="all ~ 1 + age + treated",
running_variable_name="age",
model=cp.pymc_models.LinearRegression(),
treatment_threshold=21,
)
# Visualize outputs
fig, ax = result.plot();
# Get a results summary
result.summary()
plt.show()
Roadmap
Plans for the repository can be seen in the Issues.
Videos
Click on the thumbnail below to watch a video about CausalPy on YouTube.
Features
CausalPy has a broad range of quasi-experimental methods for causal inference:
| Method | Description |
|---|---|
| Synthetic control | Constructs a synthetic version of the treatment group from a weighted combination of control units. Used for causal inference in comparative case studies when a single unit is treated, and there are multiple control units. |
| Geographical lift | Measures the impact of an intervention in a specific geographic area by comparing it to similar areas without the intervention. Commonly used in marketing to assess regional campaigns. |
| ANCOVA | Analysis of Covariance combines ANOVA and regression to control for the effects of one or more quantitative covariates. Used when comparing group means while controlling for other variables. |
| Differences in Differences | Compares the changes in outcomes over time between a treatment group and a control group. Used in observational studies to estimate causal effects by accounting for time trends. |
| Regression discontinuity | Identifies causal effects by exploiting a cutoff or threshold in an assignment variable. Used when treatment is assigned based on a threshold value of an observed variable, allowing comparison just above and below the cutoff. |
| Regression kink designs | Focuses on changes in the slope (kinks) of the relationship between variables rather than jumps at cutoff points. Used to identify causal effects when treatment intensity changes at a threshold. |
| Interrupted time series | Analyzes the effect of an intervention by comparing time series data before and after the intervention. Used when data is collected over time and an intervention occurs at a known point, allowing assessment of changes in level or trend. |
| Instrumental variable regression | Addresses endogeneity by using an instrument variable that is correlated with the endogenous explanatory variable but uncorrelated with the error term. Used when explanatory variables are correlated with the error term, providing consistent estimates of causal effects. |
| Inverse Propensity Score Weighting | Weights observations by the inverse of the probability of receiving the treatment. Used in causal inference to create a synthetic sample where the treatment assignment is independent of measured covariates, helping to adjust for confounding variables in observational studies. |
License
Support
This repository is supported by PyMC Labs.
If you are interested in seeing what PyMC Labs can do for you, then please email ben.vincent@pymc-labs.com. We work with companies at a variety of scales and with varying levels of existing modeling capacity. We also run corporate workshop training events and can provide sessions ranging from introduction to Bayes to more advanced topics.
1 maintainer
Metadata
1 maintainer
Release files for CausalPy 0.6.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| causalpy-0.6.0.tar.gz | 278.8 kB | With provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| causalpy-0.6.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 577.0 kB
Release files / causalpy-0.6.0.tar.gz
| Download URL | causalpy-0.6.0.tar.gz |
|---|---|
| Size | 278.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
e2cd9f968b87f1353548ccf0e984bb24ab4d2698fa77c938e21c1b6ae67f7d6f
|
|
BLAKE2b-256 checksum How to use checksums |
d59d6d19cde34e60fa80b201462792bbf531cbf2ca26800dcbb8533c6d89adf9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Nov 14, 2025.
Transparency logRelease files / causalpy-0.6.0-py3-none-any.whl
| Download URL | causalpy-0.6.0-py3-none-any.whl |
|---|---|
| Size | 298.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b10cdb5ddee8b1ec8b811a2a8cbc06fceaac647874d9f4a490f33f56f6a41e50
|
|
BLAKE2b-256 checksum How to use checksums |
21cc24730801ba8549272b63df98cac41a56484fa634a202bedc9514f9ec3a12
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Nov 14, 2025.
Transparency logSecurity for CausalPy 0.6.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 2 files in this release have been uploaded with provenance metadata.
Attestation for causalpy-0.6.0.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Nov 14, 2025.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
