The thesis develops "effective'' decision-making in two settings: with attention to settings where decisions have unknown effects (causal inference), and machine learning performance evaluation in algorithmic fairness, and develops "credible'' approaches for ensuring good robust performance, or otherwise evaluating sensitivity to violations of assumptions. Chapter 2 studies robust off-policy evaluation and robust decision-policy learning in a single time-step setting from observational data under unobserved confounders. Chapter 3 develops robust off-policy evaluation in a significantly more challenging infinite-horizon offline sequential setting with exogenously drawn unobserved confounders. Chapter 4 studies a different perspective on a structural assumption that is relevant from Chapter 3: rather than a setting with i.i.d. unobserved confounders, it is quite common to have a setting with exogenously drawn observed confounders, as in the case of operations research problems. Chapters 5-7 study disparity assessment for algorithmic fairness, focusing on practical challenges such as missing protected attribute and evaluating partial identification bounds, or decision-dependent censoring of outcomes. These works illustrate the importance of domain-level desiderata and specifities for even guiding methodological evaluation.