Package causallib

A package for estimating causal effect and counterfactual outcomes from observational data.

casuallib provide various causal inference methods with a distinct paradigm:

• Every causal model has some machine learning model at its core. This allows to mix & match causal models with powerful machine learning tools, simply by plugging them into the causal model.

• Inspired by the scikit-learn design, once trained, causal models can be applied onto out-of-bag samples.

causallib also provide performance evaluation scheme of the causal model by evaluating the machine learning core model in a causal inference context.

Accompanying datasets are also available, both real and simulated ones.
The various modules and folders provide the specific usage for each part.

Structure

The package is comprised of several modules, each providing a different functionality that is related to the causal inference models.

estimation

This module includes the estimator classes, where multiple popular estimators are implemented. Specifically, This includes

• Inverse probability weighting (IPW).

• Standardization.

• 3 versions of doubly-robust methods.

Each of these methods receives one or more machine learning models that can be trained (fit), and then used to estimate (predict) the relevant outcome of interest.

evaluation

This module provides the classes to evaluate the performance of methods defined in the estimation module. Evaluations are tailored to the type of method that is used. For example, weight estimators such as IPW can be evaluated for how well they remove bias from the data, while outcome models can be evaluated for their precision.

preprocessing

This module provides several enhancements to the filters and transformers provided by scikit-learn. These can be used within a pipeline framework together with the models.

datasets

Several datasets are provided within the package in the datasets module:

• NHEFS study data on the effect of smoking cessation on weight gain. Adapted from Hernán and Robins’ Causal Inference Book

• A handful of simulation sets from the 2016 Atlantic Causal Inference Conference (ACIC) data challenge.

• Simulation module allows creating simulated data based on a causal graph depicting the connection between covariates, treatment assignment and outcomes.

Additional folders

Several additional folders exist under the package and hold several internal utilities. They should only be used as part of development. This folders include analysis, simulation, utils, and tests.

Usage

The examples folder contains several notebooks exemplifying the use of the package.

Subpackages

• causallib.analysis package
  • Module contents
• Module causallib.contrib
  • Contributed Methods
  • Dependencies
  • References
  • Subpackages
    • causallib.contrib.adversarial_balancing package
      • Submodules
      • Module contents
    • Module causallib.contrib.hemm
      • Submodules
      • Module contents
    • causallib.contrib.shared_sparsity_selection package
      • Submodules
      • Module contents
  • Submodules
    • causallib.contrib.faissknn module
  • Module contents
• Module causallib.datasets
  • Datasets
  • Simulator
    • With no given data
    • Additional examples
  • License
  • Submodules
    • causallib.datasets.data_loader module
  • Module contents
• Module causallib.estimation
  • Available Methods
    • Example: Inverse Probability Weighting (IPW)
  • Submodules
    • causallib.estimation.base_estimator module
    • causallib.estimation.base_weight module
    • causallib.estimation.doubly_robust module
    • causallib.estimation.ipw module
    • causallib.estimation.marginal_outcome module
    • causallib.estimation.matching module
    • causallib.estimation.overlap_weights module
    • causallib.estimation.rlearner module
    • causallib.estimation.standardization module
    • causallib.estimation.tmle module
    • causallib.estimation.xlearner module
  • Module contents
• Module causallib.evaluation
  • Example: Inverse probability weighting
  • Submodule structure
    • evaluation.plots submodule structure
  • How to add a new plot
  • Subpackages
    • causallib.evaluation.plots package
      • Submodules
      • Module contents
  • Submodules
    • causallib.evaluation.evaluator module
    • causallib.evaluation.metrics module
    • causallib.evaluation.predictions module
    • causallib.evaluation.predictor module
    • causallib.evaluation.results module
    • causallib.evaluation.scoring module
  • Module contents
• Module preprocessing
  • Example:
    • Submodules
      • causallib.preprocessing.confounder_selection module
      • causallib.preprocessing.filters module
      • causallib.preprocessing.transformers module
    • Module contents
• causallib.simulation package
  • Submodules
    • causallib.simulation.CausalSimulator3 module
  • Module contents
• Module causallib.survival
  • Available Methods
    • Example: Weighted survival analysis with Inverse Probability Weighting
    • Example: Standardized survival (parametric g-formula)
  • Submodules
    • causallib.survival.base_survival module
    • causallib.survival.marginal_survival module
    • causallib.survival.regression_curve_fitter module
    • causallib.survival.standardized_survival module
    • causallib.survival.survival_utils module
    • causallib.survival.univariate_curve_fitter module
    • causallib.survival.weighted_standardized_survival module
    • causallib.survival.weighted_survival module
  • Module contents
• causallib.utils package
  • Submodules
    • causallib.utils.crossfit module
    • causallib.utils.general_tools module
    • causallib.utils.stat_utils module
  • Module contents

Module contents