---
title: "causal inference | Skills We Assign For | Azendo"
description: "Causal inference without experiments — quasi-experimental methods and their assumptions, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/causal-inference/"
---

[Skills](https://azendo.co/skills/) Analytics and BI 

# causal inference.

Causal inference estimates the effect of an intervention when a randomised experiment is not possible, using methods such as difference-in-differences, instrumental variables, propensity matching and synthetic controls.

## Where causal inference fits on a long engagement.

A great deal of what businesses want to evaluate cannot be randomised. Pricing changes, market launches, regulatory effects and brand campaigns are all rolled out rather than split, and the question of what they caused is still worth answering. Quasi-experimental methods are how that is done credibly.

Every method rests on assumptions that are not testable from the data alone — parallel trends, a valid instrument, no unmeasured confounding. The honest practice is stating those assumptions plainly and testing what can be tested. An estimate presented without its assumptions is an opinion with a confidence interval attached.

## What an assigned team does with causal inference.

The failure mode is not the arithmetic; it is applying a method whose assumptions the situation violates. That judgement comes from having done the analysis in context repeatedly rather than from knowing the technique.

It also depends on knowing the business well enough to name the plausible confounders, which is product knowledge accumulated over time. What that continuity commitment involves is set out in [how the monthly fee is built](https://azendo.co/pricing/).

## What we use causal inference for.

* Evaluating a change that was not split Difference-in-differences or synthetic control where a rollout could not be randomised.
* Assumptions stated with the estimate What the result depends on, made explicit rather than left implied.
* Confounders identified from domain knowledge Knowing what else changed, which no method can discover on its own.

## How causal inference capacity is assigned.

Analytical capacity is assigned under [managed data services](https://azendo.co/services/data-engineering/), with method assumptions reported alongside every estimate rather than on request.

## Roles we assign causal inference for

* [Data Scientist Data science](https://azendo.co/services/data-science/data-scientist/)

## Service lines it sits in

* [Data science](https://azendo.co/services/data-science/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in analytics and bi

* [Looker — skill we assign for](https://azendo.co/skills/looker/)
* [Power BI — skill we assign for](https://azendo.co/skills/power-bi/)
* [Tableau — skill we assign for](https://azendo.co/skills/tableau/)
* [semantic layers — skill we assign for](https://azendo.co/skills/semantic-layers/)
* [A/B testing — skill we assign for](https://azendo.co/skills/a-b-testing/)
* [experimentation design — skill we assign for](https://azendo.co/skills/experimentation-design/)
* [cohort analysis — skill we assign for](https://azendo.co/skills/cohort-analysis/)
* [segmentation — skill we assign for](https://azendo.co/skills/segmentation/)

## Tell us what your roadmap needs causal inference for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)