In this talk, we focus on automated sequential decision making which are increasingly used in socially-impactful settings like social welfare,hiring, criminal justice, etc. A particular challenge in the context of automated sequential decision making is to avoid the “perpetuation of injustice,” i.e., when maximizing utility maintains, reinforces, or even introduces unfair dependencies between sensitive features, decisions, and outcomes. In this talk, we show how to use methods from causal inference and constrained optimization to make optimal but fair decisions that would “break the cycle of injustice” by correcting for the unfair dependence of both decisions and outcomes on sensitive features.