The problem of causal inference is to determine if a given probability distribution on observed variables is compatible with some causal structure. The difficult case is when the causal structure includes latent variables. We here introduce the inflation technique for tackling this problem. An inflation of a causal structure is a new causal structure that can contain multiple copies of each of the original variables, but where the ancestry of each copy mirrors that of the original. To every distribution of the observed variables that is compatible with the original causal structure, we assign a family of marginal distributions on certain subsets of the copies that are compatible with the inflated causal structure. It follows that compatibility constraints for the inflation can be translated into compatibility constraints for the original causal structure. Even if the constraints at the level of inflation are weak, such as observable statistical independences implied by disjoint causal ancestry, the translated constraints can be strong. We apply this method to derive new inequalities whose violation by a distribution witnesses that distribution's incompatibility with the causal structure (of which Bell inequalities and Pearl's instrumental inequality are prominent examples). We describe an algorithm for deriving all such inequalities for the original causal structure that follow from ancestral independences in the inflation. For three observed binary variables with pairwise common causes, it yields inequalities that are stronger in at least some aspects than those obtainable by existing methods. We also describe an algorithm that derives a weaker set of inequalities but is more efficient. Finally, we discuss which inflations are such that the inequalities one obtains from them remain valid even for quantum (and post-quantum) generalizations of the notion of a causal model. * ewolfe@perimeterinstitute.ca † rspekkens@perimeterinstitute.ca ‡ fritz@mis.mpg.de arXiv:1609.00672v4 [quant-ph] References 44hypothesized causal relations? This is the problem that we focus on. We develop necessary conditions for a given distribution to be compatible with a given hypothesis about the causal relations.In the simplest setting, the causal hypothesis consists of a directed acyclic graph (DAG) all of whose nodes correspond to observed variables. In this case, obtaining a verdict on the compatibility of a given distribution with the causal hypothesis is simple: the compatibility holds if and only if the distribution is Markov with respect to the DAG, which is to say that the distribution features all of the conditional independence relations that are implied by d-separation relations among variables in the DAG. The DAGs that are compatible with the given distribution can be determined algorithmically [1]. 1 A significantly more difficult case is when one considers a causal hypothesis which consists of a DAG some of whose nodes correspond to latent (i.e., unobserved) variables, so that the set of observed variables corresponds to a ...