# Effects analysis

*Since version 10*

## Introduction

The **Effects Analysis** tool calculates the Causal Effect on one outcome **Y**, based on the values of one or more treatments **X**.

This is equivalent to setting an intervention on each treatment value, but this tool provides a way to automatically record the outcome **Y** for each treatment intervention, and also allows for comparison of different treatments in a chart.

For example, in the image below, we are calculating the causal effect of **Education** on **Salary** and also the causal effect of **Experience** on **Salary**.

Values on the X-axis are normalized, so when multiple treatments are present, they can be compared.

## Treatments

### Discrete Treatment

When a treatment **X** is discrete, the causal effect on the outcome **Y**, is calculated for each state in **X**, given the current evidence.

### Continuous Treatment

When a treatment **X** is continuous, the treatment **X** is discretized given the current evidence, then the causal effect on the outcome **Y** is calculated for each discretized interval of **X**.

## Outcome

## Discrete Outcome

When the outcome **Y** is discrete, and the outcome state **y** is specified, **P(Y=y | Do (X=x))** is calculated for each treatment value.

## Discretized Outcome

When the outcome **Y** is discretized, and the outcome state is not specified,
, the mean and the variance of **Y** is calculated for each treatment value.

If the outcome state is specified, the probability of the outcome is calculated instead.

## Continuous Outcome

When the outcome **Y** is continuous, the mean and the variance of **Y** is calculated for each treatment value.