In many scientific endeavors, such as GWAS, we hope to understand how manipulations of a set of inputs (causes) will affect an outcome of interest (effect). I will refer to this problem as "multi-cause causal inference". When we only have observational data, confounding from unobserved variables is a major barrier to understanding these causal relationships. In this talk, I will use simple analytical examples to illustrate the central challenges of this problem. In particular, I will show that approaches to multi-cause causal Inference resembling factor analysis are insufficient to pinpoint causal effects, even with infinite data. I'll then discuss how we can make progress in certain cases by using auxiliary negative control variables, or by shifting focus to interval estimation.