Causal inference is a powerful modeling tool for explanatory analysis, which might enable
current machine learning to become explainable. How to marry causal inference with …

The past decade has witnessed an explosion of interest in research and education in causal
inference, due to its wide applications in biomedical research, social sciences, artificial …

This paper presents the first analysis of the life course outcomes through late midlife (around
age 55) for the participants of the iconic Perry Preschool Project, an experimental high …

The Fisher randomization test (FRT) is appropriate for any test statistic, under a sharp null
hypothesis that can recover all missing potential outcomes. However, it is often sought after …

Under the potential outcomes framework, causal effects are defined as comparisons
between potential outcomes under treatment and control. To infer causal effects from …

Randomisation inference (RI) is typically interpreted as testing Fisher's 'sharp'null
hypothesis that all unit-level effects are exactly zero. This hypothesis is often criticised as …

There are two general views in causal analysis of experimental data: the super population
view that the units are an independent sample from some hypothetical infinite population …

This paper presents a simple decision-theoretic economic approach for analysing social
experiments with compromised random assignment protocols that are only partially …

N-of-1 experiments, where a unit serves as its own control and treatment in different time
windows, have been used in certain medical contexts for decades. However, due to effects …

Neyman [106]'s seminal work in 1923 has been a milestone in statistics over the century,
which has motivated many fundamental statistical concepts and methodology. In this review …