The cause2e package provides tools for performing an end-to-end causal analysis of your data. Developed by Daniel Grünbaum (@dg46).
The concept of using a LLM for developing a work plan.
DoWhy/EconML toolkit for visualizing causal paths and estimating treatment effects
This repository aims to explore all possibilities available on Microsoft's DoWhy package, based on the Causal Inference Theory and Principles.
A Streamlit web application for discovering causal relationships in your data using Microsoft's DoWhy library. This tool helps you identify and quantify causal effects between variables in your datasets through correlation-based graph discovery and rigorous causal inference.
Causal Inference for Marketplace
Internship Project on Causal Inference (The causal effect of multi-level treatment of intervention using observational data).
Causal inference system for marketing attribution using PSM, DiD, and uplift modeling with interactive Streamlit dashboard
estimate CATE and deploy uplift‑based targeting models that replace or augment traditional A/B testing.
IISc/CSA E0-294: Systems for Machine learning - Course project on employing causal insights in DNN model pruning and performance
DoWhy Streamlit app focusing on Causal Inference
Bayesian Causal Inference in Doubly Gaussian DAG-probit Models
📊 Causal Analysis of Remote Work Impact on Employee Productivity A comprehensive statistical analysis demonstrating a 7.14-point increase in employee productivity with remote work arrangements. Uses DoWhy framework and propensity score matching to establish causation beyond correlation. 🔍 Key Features: - Causal inference analysis - DoWhy
CausalAnalysisDoWhyCounterFactualNetworks_FreemiumGameplayChurn_EngineeredSyntheticData
This project is a comprehensive data science initiative focused on improving the lives of animals in shelters. We aim to leverage advanced analytical methods to predict shelter animal outcomes and optimize resource allocation.
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