What does jrfiedler/causal_inference_python_code do?

Acest repository oferă o colecție de implementări Python pentru inferența cauzală, concepute pentru a estima impactul unor intervenții specifice folosind date observaționale. Acesta servește drept set de instrumente statistice pentru cercetători, permițându-le să izoleze semnalele cauzale de factorii de confuzie complecși în seturi de date care nu beneficiază de control experimental.

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 - estimează efec…

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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.

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