Marketing Research Methods – Causal & Experimental Approaches

Overview

This repository provides practical resources for Marketing Research Methods, covering:

• Causal Inference
• Experimental & Quasi-Experimental Designs
• Reinforcement Learning Applications in Pricing
• Data Visualization & Analysis for Marketing Research

The course is designed to help researchers apply rigorous quantitative and experimental techniques in marketing studies, focusing on causality and treatment effects.

Key Topics Covered

1. Causal Inference Methods

• Conditional Average Treatment Effect (CATE) – Estimating heterogeneous treatment effects in marketing interventions.
• Difference-in-Differences (DiD) – Evaluating policy or marketing interventions.
• Instrumental Variables (IV) – Addressing endogeneity issues in observational data.
• Propensity Score Matching (PSM) – Reducing selection bias in causal studies.

2. Experimental & Quasi-Experimental Designs

• Randomized Controlled Trials (RCTs) – Understanding A/B testing and marketing experiments.
• Field & Natural Experiments – Designing and interpreting marketing studies in real-world settings.
• Regression Discontinuity Design (RDD) – Measuring causal impacts in marketing policies.
• Synthetic Control Method – Analyzing the impact of marketing interventions at scale.

3. Machine Learning for Causal Research

• Reinforcement Learning in Dynamic Pricing – Using RL models to simulate and optimize pricing strategies.
• Causal Machine Learning – Applying ML techniques (e.g., Double Machine Learning) to causal marketing studies.

4. Data Visualization & Analysis

• Basic Visualization for Marketing Insights – Exploratory data analysis (EDA) techniques.
• Experimental Data Interpretation – Using visualization to explain causality and treatment effects.

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Repository Contents

File	Description
CATE.ipynb	Conditional Average Treatment Effect (CATE) implementation
basic_visualization.ipynb	Exploratory data analysis and visualization techniques
reinforcement_learning_dynamic_pricing.ipynb	Reinforcement learning application in dynamic pricing

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Getting Started

Prerequisites

Ensure you have the following Python libraries installed:

pip install numpy pandas matplotlib seaborn scikit-learn causalml