GeoLift-SDID Library for Marketing Causal Inference

This document outlines the core components and technical advantages of the GeoLift-SDID Python library for measuring the causal impact of marketing and advertising campaigns.

Core Methodological Foundation

The GeoLift-SDID library implements the Synthetic Difference-in-Differences (SDID) methodology, a robust causal inference technique specifically optimized for geo-based marketing analysis. This approach provides a rigorous framework for isolating true incremental impact (lift) by comparing treatment geographic areas against a data-driven synthetic control group.

Technical Architecture and Implementation

Core Library Components

  1. Modular Design: The codebase has a clean separation between:

    • Core statistical methods (synthdid directory)

    • Analysis scripts (recipes directory)

    • Configuration management (configs directory)

  2. Class Hierarchy:

    • Base SyntheticDifferenceInDifferences class combines core functionality through composition

    • Specialized implementations for single-cell and multi-cell analysis

    • Robust matrix handling with fallback mechanisms for dimension constraints

  3. Robust Statistical Implementation:

    • Bootstrap standard error calculation when direct matrix computation fails

    • Matrix dimension mismatch detection and automatic fallback mechanisms

    • Proper significance testing via statistical best practices

  4. AI-Powered Interpretation:

    • Automated business interpretation using DeepSeek API

    • Standardized output formats for consistent analysis

    • First-principles break down of statistical results into business implications

Analysis Capabilities

Single-Cell Analysis

The single-cell implementation provides focused analysis on individual treatment markets:

  1. Technical Advantages:

    • Direct data extraction when model API fails due to structural constraints

    • Bootstrap resampling for standard error estimation

    • Specialized visualization for treatment vs. synthetic comparison

  2. Mathematical Robustness:

    • Handles pre/post period dimension mismatches (60 vs. 30 periods)

    • Properly calculates relative effects against appropriate baselines

    • Quantifies uncertainty through bootstrap confidence intervals

Multi-Cell Analysis

For analysis across multiple treatment markets simultaneously:

  1. Optimized Implementation:

    • Efficient parallel processing of multiple treatment units

    • Consistent statistical framework across all markets

    • Produces standardized outputs for cross-market comparison

  2. Strategic Advantages:

    • Enables portfolio-level analysis of marketing impact

    • Facilitates cross-market comparisons of effectiveness

    • Identifies relative performance drivers

Analytical Utilities

Donor Evaluation

The donor evaluator identifies optimal control markets for synthetic comparison:

  1. Evaluation Metrics:

    • Pre-treatment fit quality assessment

    • Similarity scoring through multiple correlation methods

    • Ranked recommendations based on composite metrics

  2. Technical Implementation:

    • Produces detailed diagnostic outputs

    • Configurable through YAML profiles

    • Outputs standardized recommendations for downstream analysis

Power Analysis

Calculates minimum detectable effect and statistical power:

  1. Core Functionality:

    • Simulation-based approach for power estimation

    • T-test approximation for efficient computation

    • Configurable effect sizes and test durations

  2. Practical Applications:

    • Determines required test duration for desired sensitivity

    • Verifies experiment design can detect business-relevant effects

    • Informs resource allocation for marketing tests

Recent Technical Enhancements

  1. Bootstrap Standard Error:

    • Implemented robust standard error calculation through bootstrap resampling

    • Handles cases where matrix dimension constraints prevent direct calculation

    • Provides valid p-values despite structural limitations

  2. Dimension Mismatch Handling:

    • Detects and resolves matrix dimension incompatibilities

    • Implements fallback statistical calculations

    • Maintains calculation integrity despite dimensional constraints

  3. AI Interpretation Pipeline:

    • Standardizes statistical outputs for AI consumption

    • Directly generates business-focused analysis from technical results

    • Produces actionable recommendations based on statistical findings

  4. Data Validation and Preprocessing:

    • Robust type handling for diverse input formats

    • Automatic date format conversion

    • Treatment unit validation and type alignment

Usage Guidelines

The library provides a structured workflow for marketing effectiveness measurement:

  1. Start with donor evaluation to identify optimal control markets

  2. Run power analysis to verify experimental sensitivity

  3. Conduct GeoLift analysis (single or multi-cell) to measure causal impact

  4. Generate AI interpretation for business-focused insights

See the pipeline_workflow.md document for detailed command-line examples and parameter references.

The current implementation supports both configuration-file and direct command-line parameter approaches, providing flexibility while maintaining analytical rigor.