# Advanced Causal Query

*Since version 10*

## Introduction

An **Advanced Causal Query** is useful in the following situations:

- Your Causal model contains
**Unobserved Confounders**. You can use one of the causal inference algorithms that support**Unobserved Confounders**, such as the**Backdoor adjustment**algorithm or the**FrontDoor adjustment algorithm**. - You want to use the
**Disjunctive Cause**algorithm. - You want to determine adjustment sets required for certain causal queries. e.g. using the
**Backdoor Criterion**algorithm or the**FrontDoor Criterion**algorithm. - You want to calculate the
**Direct Effect**, as some algorithms only support**Total Effect**.

[!IMPORTANT] If your causal model does not contain

Unobserved Confoundersthen you typically do not need to use theAdvanced Causal Querydialog, unless you want to computeDirect Effects. You can simply use the defaultRelevance Treealgorithm to perform causal inference, including standard Custom/Joint queries, as these other algorithms also supportInterventions(Do evidence).

## Causal Identification

The **Advanced Causal Query** dialog supports calculation of:

Adjustment sets using the

**Backdoor Criterion**algorithm, which calculates valid adjustment sets to use with the**Backdoor Adjustment/Inference**algorithm.**FrontDoor nodes**, and subsequent**adjustment sets**using the**FrontDoor Criterion**algorithm, for use with the**FrontDoor Adjustment/Inference**algorithm.

## Causal Inference

In addition to **Identification** the **Advanced Causal Query** dialog can also perform inference/adjustment.

Bayes Server supports:

- Multiple treatments (variables with Interventions)
- Multiple outcomes (e.g. Joint Query over multiple outcomes)
- Unobserved confounders (some algorithms)
- Total Effects
- Direct Effects (some algorithms)

As stated earlier If your causal model does not contain **Unobserved Confounders** then you typically do not need to use the **Advanced Causal Query** dialog, unless you need to compute **Direct Effects**.

## Total Effects

**Total Effects** includes effects that do not flow via direct links from **Treatments** -> **Outcomes**.

- e.g. Treatment -> Other Node -> Outcome
- e.g. Treatment <- Other Node -> Outcome
- etc...

This is the default in Bayes Server, and while standard algorithms such as **Relevance Tree** support interventions, they do not support **Direct Effects**.

## Direct Effects

**Direct Effects** only includes effects that flow via direct links from **Treatments** -> **Outcomes**.

- e.g. TreatmentA -> OutcomeA
- e.g. TreatmentA -> OutcomeB
- etc...