We present CEMA: Causal Explanations for Multi-Agent decision-making; a system to generate causal explanations for agents' decisions in stochastic sequential multi-agent environments. The core of CEMA is a novel causal selection method which, unlike prior work that assumes a specific causal structure, is applicable whenever a probabilistic model for predicting future states of the environment is available. We sample counterfactual worlds with this model which are used to identify and rank the salient causes behind decisions. We also designed CEMA to meet the requirements of social explainable AI. It can generate contrastive explanations based on selected causes and it works as an interaction loop with users to assure relevance and intelligibility for them. We implement CEMA for motion planning for autonomous driving and test it in four diverse simulated scenarios. We show that CEMA correctly and robustly identifies the relevant causes behind decisions and delivers relevant explanations to users' queries.