This paper serves as a starting point for machine learning researchers, engineers and students who are interested in but not yet familiar with causal inference. We start by laying out an important set of assumptions that are collectively needed for causal identification, such as exchangeability, positivity, consistency and the absence of interference. From these assumptions, we build out a set of important causal inference techniques, which we do so by categorizing them into two buckets; active and passive approaches. We describe and discuss randomized controlled trials and bandit-based approaches from the active category. We then describe classical approaches, such as matching and inverse probability weighting, in the passive category, followed by more recent deep learning based algorithms. By finishing the paper with some of the missing aspects of causal inference from this paper, such as collider biases, we expect this paper to provide readers with a diverse set of starting points for further reading and research in causal inference and discovery.

翻译：本文旨在为对因果推断感兴趣但尚不熟悉的机器学习研究者、工程师及学生提供一个入门指导。我们首先阐述一组对因果识别至关重要的假设，包括可交换性、积极性、一致性以及无干扰性。基于这些假设，我们构建了一套重要的因果推断技术，并将其分为两类：主动方法和被动方法。在主动方法中，我们描述并讨论了随机对照试验和基于bandit的方法。随后，我们在被动方法中介绍了经典技术（如匹配法和逆概率加权法），以及近年来基于深度学习的算法。通过补充本文未涉及的因果推断缺失内容（如碰撞偏差），我们期望为读者提供多样化的起点，以进一步阅读和研究因果推断与因果发现领域。