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https://github.com/amazon-science/causal-validation

Validate your causal models!
https://github.com/amazon-science/causal-validation

causal-inference economics machine-learning statistics

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Validate your causal models!

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README 

        
# Causal Validation



This package provides functionality to define your own causal data generation process

and then simulate data from the process. Within the package, there is functionality to

include complex components to your process, such as periodic and temporal trends, and

all of these operations are fully composable with one another. 



A short example is given below

```python

from causal_validation import Config, simulate

from causal_validation.effects import StaticEffect

from causal_validation.plotters import plot

from causal_validation.transforms import Trend, Periodic

from causal_validation.transforms.parameter import UnitVaryingParameter

from scipy.stats import norm



cfg = Config(

    n_control_units=10,

    n_pre_intervention_timepoints=60,

    n_post_intervention_timepoints=30,

)



# Simulate the base observation

base_data = simulate(cfg)



# Apply a linear trend with unit-varying intercept

intercept = UnitVaryingParameter(sampling_dist = norm(0, 1))

trend_component = Trend(degree=1, coefficient=0.1, intercept=intercept)

trended_data = trend_component(base_data)



# Simulate a 5% lift in the treated unit's post-intervention data

effect = StaticEffect(0.05)

inflated_data = effect(trended_data)



# Plot your data

plot(inflated_data)

```



![](https://raw.githubusercontent.com/amazon-science/causal-validation/main/static/readme_fig.png?token=GHSAT0AAAAAACTFBAFOPO3QHKOVJ26W4DU4ZWHSWFA)



## Examples



To supplement the above example, we have two more detailed notebooks which exhaustively

present and explain the functionalty in this package, along with how the generated data

may be integrated with [AZCausal](https://github.com/amazon-science/azcausal).

1. [Data Synthesis](https://amazon-science.github.io/causal-validation/examples/basic/): We here show the full range of available functions for data generation.

2. [Placebo testing](https://amazon-science.github.io/causal-validation/examples/placebo_test/): Validate your model(s) using placebo tests.

3. [AZCausal notebook](https://amazon-science.github.io/causal-validation/examples/azcausal/): We here show how the generated data may be used within an AZCausal model.



## Installation



In this section we guide the user through the installation of this package. We

distinguish here between _users_ of the package who seek to define their own data

generating processes, and _developers_ who wish to extend the existing functionality of

the package.



### Prerequisites



- Python 3.10 or higher

- [Hatch](https://hatch.pypa.io/latest/) (optional, but recommended for developers)



To install the latest stable version, run

`pip install causal-validation`

in your terminal.



### For Users



1. It's strongly recommended to use a virtual environment. Create and activate one using your preferred method before proceeding with the installation.

2. Clone the package `git clone [email protected]:amazon-science/causal-validation.git`

3. Enter the package's root directory `cd causal-validation`

4. Install the package `pip install -e .`



### For Developers



1. Follow steps 1-3 from `For Users`

2. Create a hatch environment `hatch env create`

3. Open a hatch shell `hatch shell`

4. Validate your installation by running `hatch run dev:test`