BestHub
Sign in
Tagged articles
7 articles
Page 1 of 1
JD.com Experience Design Center
JD.com Experience Design Center
Jul 5, 2023 · Product Management

How Causal Inference Can Unlock High‑Impact Product Requirements

This article reviews a product‑manager’s end‑to‑end workflow for forecasting demand value and validating hypotheses, illustrating how Wallace’s scientific loop translates to business, and detailing causal‑inference techniques such as matching, DID, regression discontinuity, and instrumental variables with a real‑world case study.

causal inferencedata analysiseconometrics
0 likes · 17 min read
How Causal Inference Can Unlock High‑Impact Product Requirements
Model Perspective
Model Perspective
Feb 27, 2023 · Fundamentals

Mastering Difference-in-Differences: Theory, Example, and Python Implementation

Learn how the Difference-in-Differences (DiD) method estimates policy impacts by comparing treatment and control groups over time, explore its mathematical model, see a concrete traffic‑restriction example, and follow a step‑by‑step Python implementation with data analysis and visualization.

Pythondifference-in-differenceseconometrics
0 likes · 10 min read
Mastering Difference-in-Differences: Theory, Example, and Python Implementation
DataFunTalk
DataFunTalk
Dec 26, 2022 · Artificial Intelligence

A Review of Causal Inference Methods: Potential Outcomes, Structural Causal Models, and Recent Advances

This article reviews the two main streams of causal inference—potential‑outcome (Rubin) models and structural causal (Pearl) diagrams—covers classic techniques such as A/B testing, instrumental variables, matching, difference‑in‑differences, synthetic controls, matrix completion, heterogeneous treatment effect estimation, and discusses modern machine‑learning‑based approaches and causal discovery algorithms.

A/B testingcausal inferenceeconometrics
0 likes · 33 min read
A Review of Causal Inference Methods: Potential Outcomes, Structural Causal Models, and Recent Advances
Model Perspective
Model Perspective
Aug 11, 2022 · Fundamentals

Master VAR Modeling: Theory, Workflow, and Full Python Implementation

This guide explains the theory behind Vector Autoregression (VAR) models, outlines the complete modeling workflow—including data preparation, stationarity and cointegration testing, lag order selection, parameter estimation, stability diagnostics, and impulse‑response and variance‑decomposition analysis—and provides a full Python implementation with code examples.

ModelingPythonTime Series
0 likes · 9 min read
Master VAR Modeling: Theory, Workflow, and Full Python Implementation
Model Perspective
Model Perspective
Jul 21, 2022 · Artificial Intelligence

Tackling Multicollinearity: Ridge and LASSO Regression Explained with Python

This article explains how multicollinearity undermines ordinary least squares estimates, introduces ridge and LASSO regularization as remedies, and demonstrates their application on a 1966 French economic dataset using Python’s statsmodels, complete with code and interpretation of results.

LASSOPythonRegularization
0 likes · 7 min read
Tackling Multicollinearity: Ridge and LASSO Regression Explained with Python