quantitive-marketing

Here is my notes on regression discontinuity from Prof. Ding’s textbook (2024) and Prof. Imai’s lecture notes. Motivation We often cannot run a randomized experiment and have to use/design observational studies to find a setting where credible causal inference is possible.

Motivation Endogeneity: When an explanatory variable (treatment) is correlated with the unobserved error term (e.g., due to omitted variables, measurement error, or simultaneity). Consequence: Standard regression (e.g., OLS) yields biased estimates.

Here’s a overview of instrument-free methods: Latent Instrumental Variable (LIV) Gaussian Copula (GC) The goal is for dealing with endogeneity when no external instruments are available. Motivation Finding good instruments is hard.

In quantitative marketing, unobserved heterogeneity refers to differences in consumer response parameters that cannot be explained by observed demographics or past behavior. Modeling this form of heterogeneity is crucial for uncovering true segmentation, avoiding bias from aggregation, and capturing variation in decision rules across consumers.

1. Introduction This guide provides practical techniques for increasing the statistical power of marketing experiments without relying solely on large sample sizes. Based on Meyvis and van Osselaer’s work (2018), these methods enable researchers to detect subtle marketing effects with feasible sample sizes by increasing observed effect sizes through proper design and analysis decisions.

Motivation Uncovering the insights from User-generated content (UGC) is important for researchers as UGC provides a rich information about consumer’s experiences with quality. Tirunillai and Tellis (2014) propose a unified framework:

last update: 06-05-2025 Introduction Establishing causation from observational data remains one of the most challenging tasks in empirical research. This guide examines twelve major causal inference methods, from traditional econometric approaches (DiD, IV, RD) to modern machine learning techniques (Causal Forests, DML, Auto-DML).

Check out igraph R-package 📦! Summary of Concepts See my notes in “Analyzing Social Media Networks with NodeXL: Insights from A Connected World” (Hansen et al. 2019), Chapter 3 Sections 3.