Causal Impact Analysis as an Alternative to A/B Testing

Welcome to a two-part exploration of Causal Impact analysis. In this concise overview, we aim to equip you with theoretical foundations and practical insights.

The first part of the current article provides a quick dive into the theoretical aspects of Causal Impact analysis. Discover how this method operates, its suitability, and essential limitations.

The second part delves into the practical application of Causal Impact analysis. We guide you through a specific dataset, demonstrating how to implement the library and interpret results. This hands-on approach empowers you to not only use the Causal Impact library effectively but also draw meaningful conclusions from your analyses.

Introduction

In the current work of data analysts, evaluating the effectiveness after the introduction of a particular feature is one of the most important tasks. While randomized experiments like AB tests are the traditional gold standard for effect estimation, real-world challenges sometimes make their implementation impractical.

The following common scenarios illustrate these challenges.

• Companies may lack the inherent infrastructure or suitability for experiments
• The inability to experiment due to geographical limitations. For example, you have launched a broad brand marketing company
• Practical constraints such as cost, resource limitations, or the need to analyze past actions

In these situations, need to find alternative methods to estimate the feature’s effects and understand the impact of changes except for AB tests. This article explores the CausalImpact library, an open-source tool developed by Google.

What is CausalImpact?

At its heart, causal impact is about figuring out the impact of an action by guessing how a certain measure would have changed if that action (like testing a feature) hadn't happened.

We predict this hypothetical change, compare it to what actually occurred, and the gap between them tells us how much the action influenced the outcome.

Imagine launching a wide advertising campaign in the UK to promote a new app feature. The goal is to increase installations by reaching a larger audience through bloggers. However, placing part of this audience in a control group, where they don't see the new feature, might create a negative impression. To address this, a decision is made to roll out the feature for the entire Region B, while Region A serves as the control group without the brand campaign.

Here's how CausalImpact comes into play:

Control and Test Groups:

• Region A (Control Group): No brand campaign.
• Region B (Test Group): Brand campaign introduced to the audience.

Targeted Metric:

• The metric of interest is the number of installations.

Date of Intervention:

• The date when the campaign is rolled out to all users in Region B.

With CausalImpact, a model is built based on installs from Region A. This model then predicts the expected values for the same time period in Region B, assuming no brand campaign occurred. These predicted values serve as a baseline, representing what could be expected in Region B without the advertising campaign.

The important step involves comparing the actual results in Region B against these expected results. This comparison reveals the impact of the advertising campaign on installations.

CausalImpact essentially allows us to quantify and understand how much the intervention influenced the outcome, providing valuable insights for decision-makers and data scientists.

Limitations of Using Causal Impact Analysis

Causal Impact analysis has its limitations that practitioners need to be mindful of:

1. Data Quality Matters:
   • Causal Impact's effectiveness relies on the quality of the data it receives. Inaccurate or incomplete data can compromise the reliability of the analysis.
2. Correlation in Control Group:
   • Control group must exhibit reasonable correlation with test groups. It's crucial to ensure that the chosen control group is comparable to the test group to enhance the accuracy of predictions.
3. Macro Event Impacts:
   • Both test and control groups should be affected by macro events in a similar manner. Any significant disparities in external factors can introduce bias into the analysis, affecting the accuracy of causal effect estimation.
4. Post-Analysis Considerations:
   • What happens in the control groups during the post-analysis period is vital. Interrupting regular marketing activities or making changes in the control groups during this period can lead to misleading results. It's essential to maintain consistency in control group conditions.
5. External Influences:
   • External factors beyond the scope of the analysis can also impact the results. Understanding and accounting for these external influences is necessary for a more accurate interpretation of causal effects.

Being aware of these limitations and carefully addressing them in the analysis process is crucial to obtaining meaningful insights and making informed decisions based on Causal Impact analysis.

In the next article, I will give a practical example of using the library and tell you how to interpret the results