What are the main features of jrfiedler/causal_inference_python_code?

The main features of jrfiedler/causal_inference_python_code are: Causal Effect Estimators, Causal Inference, G-Formula Estimators, G-Formula Standardizers, Regression Analysis, Causal Counterfactual Simulators, Data Science, Observational Data Analysis Tools.

What are some open-source alternatives to jrfiedler/causal_inference_python_code?

Open-source alternatives to jrfiedler/causal_inference_python_code include: py-why/dowhy — DoWhy is an open-source Python library for causal inference that structures the entire analysis into a sequential… py-why/econml — EconML is a Python library for causal inference designed to estimate heterogeneous treatment effects using a… uber/causalml — CausalML is a machine learning library for causal inference, providing tools to estimate treatment effects and causal… rmcelreath/stat_rethinking_2022 — This project is a collection of Bayesian statistics courseware and educational resources. It provides instructional… statsmodels/statsmodels — Statsmodels is a comprehensive Python library designed for statistical modeling, econometric research, and data… camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers — This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for…

Causal Inference Python Code - डेटा से कॉज़ल…

This repository provides a collection of Python implementations for causal inference, designed to estimate the impact of specific interventions using observational data. It serves as a statistical toolkit for researchers to isolate causal signals from complex confounding factors in data sets that lack experimental control.

The framework enables the application of rigorous methodologies to study health determinants and evaluate policy interventions. By utilizing structural causal modeling and directed acyclic graphs, the library allows users to map causal dependencies and identify the necessary variables for unbiased estimation. It supports the simulation of counterfactual outcomes to compare potential results under different treatment scenarios, providing a structured approach to understanding cause and effect.

The toolkit covers a broad range of statistical estimation techniques, including inverse probability weighting, g-formula computation, and parametric regression analysis. These computational tools are organized to facilitate the analysis of observational data within epidemiological and social research contexts. The project is distributed as a collection of Jupyter Notebooks containing these statistical frameworks and implementations.

Features

Causal Effect Estimators - Calculates the impact of specific interventions on outcomes by applying statistical methodologies to observational data sets.
Causal Inference - Provides a collection of statistical methods for estimating causal effects from observational data using techniques like inverse probability weighting and g-formula.
G-Formula Estimators - Estimates causal effects by standardizing the distribution of outcomes across treatment groups using conditional probability models.
G-Formula Standardizers - Estimates causal effects by integrating conditional probability models to simulate outcomes across different hypothetical treatment distributions.
Regression Analysis - Uses linear or generalized models to quantify the relationship between treatment variables and outcomes while controlling for covariates.
Causal Counterfactual Simulators - Simulates potential outcomes under different treatment scenarios to estimate the difference between observed and hypothetical states.
Data Science - Implements computational techniques to identify and quantify cause and effect relationships within data sets lacking experimental control.
Observational Data Analysis Tools - Analyzes data collected without experimental control to identify potential causal relationships between variables using structured statistical frameworks.
Observational Data Processors - Applies rigorous statistical frameworks to non-experimental data sets to isolate causal signals from complex confounding factors.
Epidemiological Research Methodologies - Applies rigorous causal inference methodologies to study determinants of health and evaluate the impact of policy interventions.
Epidemiological Analysis Frameworks - Uses structured statistical frameworks to study health determinants and evaluate the potential effects of public health policy interventions.
Epidemiological Research Methods - Applies advanced statistical techniques to study the distribution and determinants of health-related states or events in specified populations.
Inverse Probability Weighting Estimators - Adjusts for selection bias by weighting observations based on the inverse of their predicted probability of receiving a treatment.
Statistical Estimation - Fits generalized linear models to observational data to quantify the relationship between interventions and observed outcomes.
Causal Effect Estimators - Calculates causal parameters by fitting mathematical models to observational data to isolate the effect of specific interventions.
Public Health Policy Evaluators - Assesses the potential outcomes of policy interventions by modeling causal pathways to inform evidence-based decision making.
Research and Data Analysis Tools - Facilitates the modeling of counterfactual outcomes and the evaluation of intervention impacts through structured research workflows.
Statistical Analysis Libraries - Offers computational tools for calculating the impact of interventions on outcomes within complex research data sets.
Directed Acyclic Graph Models - Maps causal dependencies using visual diagrams to identify necessary variables for unbiased statistical estimation.

DoWhy is an open-source Python library for causal inference that structures the entire analysis into a sequential four-step framework: modeling, identification, estimation, and refutation. It treats causal assumptions as explicit, first-class citizens, represented as directed acyclic graphs that can be automatically validated against observed data. The library distinguishes itself by cleanly separating the causal identification problem from statistical estimation, allowing any compatible estimator to be used for a given target estimand. It includes automated refutation testing that validates

EconML is a Python library for causal inference designed to estimate heterogeneous treatment effects using a combination of machine learning and econometrics. It serves as a toolkit for calculating conditional average treatment effects to determine how specific interventions impact individuals or subgroups. The project provides a framework for double machine learning and orthogonal machine learning to isolate causal signals from high-dimensional confounders. It includes specialized implementations for causal forests and instrumental variable learners, allowing for the recovery of causal relat

CausalML is a machine learning library for causal inference, providing tools to estimate treatment effects and causal impacts using experimental and observational data. It functions as a framework for uplift modeling and the estimation of heterogeneous treatment effects to distinguish causation from correlation. The library focuses on identifying how different user segments respond to specific interventions. This includes calculating the incremental gain of target metrics to optimize marketing campaigns, targeting high-response customer segments, and personalizing user engagement through the

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rmcelreath/stat_rethinking_2022

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This project is a collection of Bayesian statistics courseware and educational resources. It provides instructional materials, problem sets, and solutions designed for learning Bayesian data analysis and causal modeling. The repository includes a suite of statistical data visualization scripts used to generate instructional animations and plots. It also contains code examples that implement Bayesian modeling and survival analysis across multiple programming languages to demonstrate different computational approaches. The materials cover a range of statistical capabilities, including causal i

jrfiedlercausal_inference_python_code