Causal Effect Estimation
^^^^^^^^^^^^^^^^^^^^^^^^

The Neyman-Rubin Potential Outcomes Framework is an approach for estimating
causal effects (also known as treatment effects) in causal inference. It defines
causality through the tensor outcomes :math:`Y` of a binary intervention :math:`T`.
The causal effect, defined as the difference between these tensor outcomes,
is the core focus of this framework. :cite:t:`rubin2005causal`

However, since only one of the tensor outcomes is observed—either the unit receives
the intervention or it does not—the difference in *tensor* outcomes is unobservable.
This is known as the "Fundamental Problem of Causal Inference".

Recent advancements have developed improved statistical estimators for causal effects,
each associated with specific causal assumptions. This module integrates these
advancements with foundational causal identification through Bayesian networks.
:cite:t:`pearl1995causal`
It provides a pipeline for detecting suitable adjustment sets and applying the
appropriate estimators to achieve accurate causal effect estimations.

.. autoclass:: pyagrum.causal.CausalEffectEstimation

.. bibliography::
    :filter: docname in docnames
