The era of behavioural big data has created new avenues for data science research, with
many new contributions stemming from academic researchers. Yet data controlled by …

This book builds on and is a sequel to our book Targeted Learning: Causal Inference for
Observational and Experimental Studies (2011). Since the publication of this first book on …

Estimating the effects of interventions in networks is complicated due to interference, such
that the outcomes for one experimental unit may depend on the treatment assignments of …

Peer effects, in which the behavior of an individual is affected by the behavior of their peers,
are central to social science. Because peer effects are often confounded with homophily and …

We consider experiments in dynamical systems where interventions on some experimental
units impact other units through a limiting constraint (such as a limited supply of products) …

Estimation of social influence in networks can be substantially biased in observational
studies due to homophily and network correlation in exposure to exogenous events …

Online evaluation is one of the most common approaches to measure the effectiveness of an
information retrieval system. It involves fielding the information retrieval system to real users …

Many public health interventions provide benefits that extend beyond their direct recipients
and impact people in close physical or social proximity who did not directly receive the …

Standard estimators of the global average treatment effect can be biased in the presence of
interference. This paper proposes regression adjustment estimators for removing bias due to …

Interference is a ubiquitous problem in experiments conducted on two-sided content
marketplaces, such as Douyin (China's analog of TikTok). In many cases, creators are the …