This paper discusses the problem of estimation and inference on the effects of time-varying
treatment. We propose a method for inference on the effects treatment histories, introducing …

Data from both a randomized trial and an observational study are sometimes simultaneously
available for evaluating the effect of an intervention. The randomized data typically allows for …

We introduce trajectory balancing, a general reweighting approach to causal inference with
time-series cross-sectional (TSCS) data. We focus on settings in which one or more units is …

In this paper we present a continuous extension for longitudinal analysis settings of the
recently proposed Covariate Balancing Propensity Score (CBPS) methodology. While …

Randomized trials and observational studies, more often than not, run over a certain period
of time. The treatment effect evolves during this period which provides crucial insights into …

Propensity score matching and inverse-probability weighting are popular methods for causal
inference in observational studies. Under the assumption of unconfoundedness, these …

We consider the task of identifying and estimating the causal effect of a treatment variable on
a long-term outcome variable using data from an observational domain and an experimental …

This paper studies statistical decisions for dynamic treatment assignment problems. Many
policies involve dynamics in their treatment assignments where treatments are sequentially …

The estimation of average treatment effects based on observational data is extremely
important in practice and has been studied by generations of statisticians under different …

Experiments that use covariate adaptive randomization (CAR) are commonplace in applied
economics and other fields. In such experiments, the experimenter first stratifies the sample …