Mila Tea Talk (2020)
Valid Causal Inference with (Some) Invalid Instruments
Abstract
Instrumental variable (IV) methods provide a powerful approach to estimating causal effects: they are robust to unobserved confounders and they can be combined with deep networks for flexible nonlinear causal effect estimation. But a key challenge when applying them is the reliance on untestable “exclusion” assumptions. In this talk, I will discuss recent work where we showed how to perform consistent IV estimation despite some violations of these assumptions. In particular, we show that when one has multiple candidate instruments, only a majority of these candidates—or, more generally, the modal candidate-response relationship—needs to be valid to estimate the causal effect. Our approach, ModeIV, uses an estimate of the modal prediction from an ensemble of instrumental variable estimators. The technique is simple to apply and is “black-box” in the sense that it may be used with any instrumental variable estimator as long as the treatment effect is identified for each valid instrument independently.