Had I left 5 minutes earlier, I would have caught the bus. Had I been driving slower, I would have avoided the accident. Counterfactual thoughts—“what if?” scenarios about outcomes contradicting what actually happened—play a key role in everyday human reasoning and decision-making. In conjunction with rapid advancements in the mathematical study of causality, there has been an increasing interest in the development of machine learning methods that support elements of counterfactual reasoning, i.e., they make predictions about outcomes that "could have been different". Such methods find applications in a wide variety of domains ranging from personalized healthcare and explainability to AI safety and offline reinforcement learning. Although the research at the intersection of causal inference and machine learning is blooming, there has been no venue so far explicitly focusing on methods involving counterfactuals. In this workshop, we aim to fill that space by facilitating interdisciplinary interactions that will shed light onto the three following questions: (i) What insights can causal machine learning take from the latest advances in cognitive science? (ii) In what use cases is each causal modeling framework most appropriate for modeling counterfactuals? (iii) What barriers need to be lifted for the wider adoption of counterfactual-based machine learning applications, like personalized healthcare?