---
title: Interaction Detection
description: Learn how to detect interactions between overlapping experiments and understand their impact.
product: general
token_estimate: 1413
---
# Interaction Detection

> For AI agents: a documentation index is available at [/llms.txt](/llms.txt). Append `.md` to any page URL for markdown, or send `Accept: text/markdown`.

## What is interaction detection

When you run overlapping experiments, the experiments can interfere with each other. Interaction Detection lets you pick two experiments and evaluate them for interaction. Interaction Detection helps you understand whether users exposed to both experiments behave differently from users exposed to only one. The core question is: **Does being in both experiments change the effect on a metric in a way that differs from summing the individual effects?**

## Should I worry about it

The general guidance is to run overlapping experiments. Users viewing your landing page may experience multiple experiments at the same time. Running overlapping experiments is [supported by industry experience](https://www.statsig.com/blog/embracing-overlapping-a-b-tests-and-the-danger-of-isolating-experiments) and [published research](https://www.microsoft.com/en-us/research/articles/a-b-interactions-a-call-to-relax/). Teams expecting to run conflicting experiments can avoid conflicts by making experiments mutually exclusive using [Layers](https://docs.statsig.com/experiments/layers-overview) (also called Universes).

## How to use interaction detection

To start an Interaction Detection analysis, go to _Experiment > Results > Explore > Interaction Effect Detection_.

![Interaction Detection interface in Statsig console](https://docs.statsig.com/images/experiments/exploring-results/interaction-detection/b49e62b2-ba95-4db3-8d5a-3ce3f08b620d.png)

Select a second experiment and the metrics to analyze. Statsig then shows a summary with three sections:

- **By Groups**: unit counts based on the collective slice of group assignment
- **Metric Summary Results**: estimated intervals of difference in metric lift
- **Overlapping Unique Users**: an overview of the general traffic intersection of the two experiments

![Interaction Detection analysis results dashboard](https://docs.statsig.com/images/experiments/exploring-results/interaction-detection/ebbb4383-8abe-4b9b-9132-fb543e6b33ac.png)

Refer to this [article](https://www.statsig.com/blog/interaction-effect-detection) for additional examples.

## Methodology

Assume you have two experiments A and B, each with two groups: control and test. The interaction effect calculates the overlapping impact on users exposed in both experiment A and experiment B.

$$
\Delta_{\text{treatment effect}} = (\overline{X_{testA \cdot testB}} - \overline{X_{testA \cdot controlB}}) - (\overline{X_{controlA \cdot testB}} - \overline{X_{controlA \cdot controlB}})
$$

$$
\Delta_{\text{treatment effect}}{\%} = \frac{\Delta_{\text{treatment effect}}}{(\overline{X_{controlA \cdot testB}} - \overline{X_{controlA \cdot controlB}})}
$$

$$
variance = \frac{Var(X_{testA \cdot testB})}{n_{testA \cdot testB}} + \frac{Var(X_{testA \cdot controlB})}{n_{testA \cdot controlB}} + \frac{Var(X_{controlA \cdot testB})}{n_{controlA \cdot testB}} + \frac{Var(X_{controlA \cdot controlB})}{n_{controlA \cdot controlB}}
$$

### Intuition

- The part $\overline{X_{testA \cdot testB}} - \overline{X_{testA \cdot controlB}}$ answers: “If someone is in A’s test, how much does changing B from control to test change the metric?”
- The part $\overline{X_{controlA \cdot testB}} - \overline{X_{controlA \cdot controlB}}$ answers: “If someone is in A’s control, how much does changing B from control to test change the metric?”
- Subtracting those two values shows whether the effect of B differs depending on whether the user is in A’s test or control group. That difference is the interaction effect.

If the result isn’t statistically significant, the effect of B is the same regardless of which group the user is in for A (no significant interaction). If it’s statistically significant, the effect of B depends on A’s assignment (significant interaction).

### Directionality

The direction (positive or negative) of the interaction effect indicates whether the combination of the two treatments amplifies or dampens each other’s effects.

Statsig defines the interaction effect as:

Interaction Effect = (Effect of B when A = Test) − (Effect of B when A = Control)

- **Statistically positive interaction**: B’s effect is stronger when A is also active.
- **Statistically negative interaction**: B’s effect is weaker (or reversed) when A is active.

### Magnitude

The magnitude (absolute size) of the interaction effect indicates how much the combined impact deviates from simple additivity.

For example:

- Experiment A produces a **+5%** lift on your metric of interest
- Experiment B produces a **+5%** lift on your metric
- The interaction effect between A and B is **–3%**

The –3% interaction effect means that rolling out both experiments together produces an overall impact about 3% lower than the sum of their individual effects. The features interfere with each other.

To obtain the most accurate estimate of the true combined impact, run a new experiment that includes both features together. Experiments A and B may not have run over exactly the same time period or under identical conditions. These differences in timing and seasonality can slightly influence the measured interaction magnitude.

> **Info:**
>
> When an experiment includes more than two groups, Statsig evaluates interaction effects **pairwise between groups**. You can view the interaction effect for specific group combinations by selecting the groups you want in the **”Select Comparison”** dropdown, as the UI above shows.