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In this talk I will discuss two (controversial) proposals: 1. To account for the probability of conditionals in terms of objective chances. I will argue for the naturalness of such an account from a Bayesian learning perspective, and argue that it is beneficial as well to account for embedded conditionals and learning from conditionals. 2. I will argue that a causal power analysis of conditionals is compatible with the existence of ‘irrelevance’ conditionals like ‘Even if Mary comes to the party, Bill will be unhappy’. If time permits, I will finally defend a causal analysis of correlative constructions like ‘The sooner this pandemic is over, the better’. (Some of this work is joint with Katrin Schulz.)