# 5. Comparison to other packages

DoWhy’s effect inference API captures all four steps of causal inference:

Model a causal inference problem using assumptions.

Identify an expression for the causal effect under these assumptions (“causal estimand”).

Estimate the expression using statistical methods such as matching or instrumental variables.

Finally, verify the validity of the estimate using a variety of robustness checks.

This workflow is captured by four key verbs in DoWhy:

model

identify

estimate

refute

Using these verbs, DoWhy implements a causal inference engine that can support
a variety of methods. *model* encodes prior knowledge as a formal causal graph, *identify* uses
graph-based methods to identify the causal effect, *estimate* uses
statistical methods for estimating the identified estimand, and finally *refute*
tries to refute the obtained estimate by testing robustness to assumptions.

## 5.1. Key difference: Causal assumptions as first-class citizens

Due to DoWhy’s focus on the full pipeline of causal analysis (not just a single step), there are three differences compared to available software for causal inference.

**Explicit identifying assumptions**Assumptions are first-class citizens in DoWhy.

Each analysis starts with a building a causal model. The assumptions can be viewed graphically or in terms of conditional independence statements. Wherever possible, DoWhy can also automatically test for stated assumptions using observed data.

**Separation between identification and estimation**Identification is the causal problem. Estimation is simply a statistical problem.

DoWhy respects this boundary and treats them separately. This focuses the causal inference effort on identification, and frees up estimation using any available statistical estimator for a target estimand. In addition, multiple estimation methods can be used for a single identified_estimand and vice-versa.

**Automated robustness checks**What happens when key identifying assumptions may not be satisfied?

The most critical, and often skipped, part of causal analysis is checking the robustness of an estimate to unverified assumptions. DoWhy makes it easy to automatically run sensitivity and robustness checks on the obtained estimate.

Finally, DoWhy is easily extensible, allowing other implementations of the
four verbs to co-exist (e.g., we support implementations of the *estimation* verb from
EconML and CausalML libraries). The four verbs are mutually independent, so their
implementations can be combined in any way.