**Average treatment effect**

Randomized control trials are gold standard for collecting data for machine learning implementations based on causal inference. Scientists and engineers utilize predictive capabilities of machine learning to do counterfactual regression which eventually helps to calculate average treatment effect, given as

*ATE=E[Y(1)-Y(0)]*

In Machine learning, we have flexibility of training multiple models with algorithms and neural networks to measure ATE by counterfactual prediction. ATE is considered as the most important metric in causal inference because it gives a clear indication of which treatment should be considered and finalized for the expected outcome.

**Machine Learning for Causal inference.**

Some established methods for ATE are Inverse propensity weighing, matching, doubly robust estimations, propensity score matching along with these there are various meta-learning algorithms can be utilised based on treatment imbalance such as S-Learner, T-Learner etc.

In research problems we experiment on algorithms that can perform counterfactual regression for multiple treatment rather than binary treatment and for continuous treatment such as treatment dosage.

**Causal Inference in Business Analytics**

One of the most common use-case of causal inference in business analytics is Uplift Modelling