Alan M. Jacobs

There is a growing consensus in the social sciences on the virtues of research strategies that combine quantitative with qualitative tools of inference. Integrated Inferences develops a framework for using causal models and Bayesian updating for qualitative and mixed-methods research. By making, updating, and querying causal models, researchers are able to integrate information from different data sources while connecting theory and empirics in a far more systematic and transparent manner than standard qualitative and quantitative approaches allow. This book provides an introduction to fundamental principles of causal inference and Bayesian updating and shows how these tools can be used to implement and justify inferences using within-case (process tracing) evidence, correlational patterns across many cases, or a mix of the two. The authors also demonstrate how causal models can guide research design, informing choices about which cases, observations, and mixes of methods will be most useful for addressing any given question.

There is a growing consensus in the social sciences on the virtues of research strategies that combine quantitative with qualitative tools of inference. Integrated Inferences develops a framework for using causal models and Bayesian updating for qualitative and mixed-methods research. By making, updating, and querying causal models, researchers are able to integrate information from different data sources while connecting theory and empirics in a far more systematic and transparent manner than standard qualitative and quantitative approaches allow. This book provides an introduction to fundamental principles of causal inference and Bayesian updating and shows how these tools can be used to implement and justify inferences using within-case (process tracing) evidence, correlational patterns across many cases, or a mix of the two. The authors also demonstrate how causal models can guide research design, informing choices about which cases, observations, and mixes of methods will be most useful for addressing any given question.

In Governing for the Long Term, Alan M. Jacobs investigates the conditions under which elected governments invest in long-term social benefits at short-term social cost. Jacobs contends that, along the path to adoption, investment-oriented policies must surmount three distinct hurdles to future-oriented state action: a problem of electoral risk, rooted in the scarcity of voter attention; a problem of prediction, deriving from the complexity of long-term policy effects; and a problem of institutional capacity, arising from interest groups' preferences for distributive gains over intertemporal bargains. Testing this argument through a four-country historical analysis of pension policymaking, the book illuminates crucial differences between the causal logics of distributive and intertemporal politics and makes a case for bringing trade-offs over time to the center of the study of policymaking.

In Governing for the Long Term, Alan M. Jacobs investigates the conditions under which elected governments invest in long-term social benefits at short-term social cost. Jacobs contends that, along the path to adoption, investment-oriented policies must surmount three distinct hurdles to future-oriented state action: a problem of electoral risk, rooted in the scarcity of voter attention; a problem of prediction, deriving from the complexity of long-term policy effects; and a problem of institutional capacity, arising from interest groups' preferences for distributive gains over intertemporal bargains. Testing this argument through a four-country historical analysis of pension policymaking, the book illuminates crucial differences between the causal logics of distributive and intertemporal politics and makes a case for bringing trade-offs over time to the center of the study of policymaking.