Average treatment effect

The average treatment effect (ATE) is a measure used to compare treatments (or interventions) in randomized experiments, evaluation of policy interventions, and medical trials. The ATE measures the difference in mean (average) outcomes between units assigned to the treatment and units assigned to the control. In a randomized trial (i.e., an experimental study), the average treatment effect can be estimated from a sample using a comparison in mean outcomes for treated and untreated units. However, the ATE is generally understood as a causal parameter (i.e., an estimate or property of a population) that a researcher desires to know, defined without reference to the study design or estimation procedure. Both observational studies and experimental study designs with random assignment may enable one to estimate an ATE in a variety of ways. General definition Originating from early statistical analysis in the fields of agriculture and medicine, the term "treatment" is now appli

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (6)

Related publications

Related people (1)

Related people

Related units (1)

Related units

Related concepts (5)

Related concepts

Related courses (4)

Related courses

Related lectures (13)

Related lectures

Transforming cumulative hazard estimates

Pal Christie Ryalen, Mats Julius Stensrud

Time-to-event outcomes are often evaluated on the hazard scale, but interpreting hazards may be difficult. Recently in the causal inference literature concerns have been raised that hazards actually have a built-in selection bias that prevents simple causal interpretations. This is a problem even in randomized controlled trials, where hazard ratios have become a standard measure of treatment effects. Modelling on the hazard scale is nevertheless convenient, for example to adjust for covariates; using hazards for intermediate calculations may therefore be desirable. In this paper we present a generic method for transforming hazard estimates consistently to other scales at which these built-in selection biases are avoided. The method is based on differential equations and generalizes a well-known relation between the Nelson–Aalen and Kaplan–Meier estimators. Using the martingale central limit theorem, we show that covariances can be estimated consistently for a large class of estimators, thus allowing for rapid calculation of confidence intervals. Hence, given cumulative hazard estimates based on, for example, Aalen’s additive hazard model, we can obtain many other parameters without much more effort. We give several examples and the associated estimators. Coverage and convergence speed are explored via simulations, and the results suggest that reliable estimates can be obtained in real-life scenarios.

2018

Feedback and Mediation in Causal Inference Illustrated by Stochastic Process Models

Mats Julius Stensrud

The concept of causality is naturally related to processes developing over time. Central ideas of causal inference like time-dependent confounding (feedback) and mediation should be viewed as dynamic concepts. We shall study these concepts in the context of simple dynamic systems. Time-dependent confounding and its implications are illustrated in a Markov model. We emphasize the distinction between average treatment effect, ATE, and treatment effect of the treated, ATT. These effects could be quite different, and we discuss the relationship between them. Mediation is studied in a stochastic differential equation model. A type of natural direct and indirect effects is considered for this model. Mediation analysis of discrete measurements from such processes may give misleading results, and one needs to consider the underlying continuous process. The dynamic and time-continuous view of causality and mediation is an essential feature, and more attention should be payed to the time aspect in causal inference.

2018

Separable Effects for Causal Inference in the Presence of Competing Events

Mats Julius Stensrud

In time-to-event settings, the presence of competing events complicates the definition of causal effects. Here we propose the new separable effects to study the causal effect of a treatment on an event of interest. The separable direct effect is the treatment effect on the event of interest not mediated by its effect on the competing event. The separable indirect effect is the treatment effect on the event of interest only through its effect on the competing event. Similar to Robins and Richardson’s extended graphical approach for mediation analysis, the separable effects can only be identified under the assumption that the treatment can be decomposed into two distinct components that exert their effects through distinct causal pathways. Unlike existing definitions of causal effects in the presence of competing events, our estimands do not require cross-world contrasts or hypothetical interventions to prevent death. As an illustration, we apply our approach to a randomized clinical trial on estrogen therapy in individuals with prostate cancer. Supplementary materials for this article are available online.

2020

MICRO-110: Design of experiments

This course provides an introduction to experimental statistics, including use of population statistics to characterize experimental results, use of comparison statistics and hypothesis testing to evaluate validity of experiments, and design, application, and analysis of multifactorial experiments

MGT-581: Introduction to econometrics

The course provides an introduction to econometrics. The objective is to learn how to make valid (i.e., causal) inference from economic data. It explains the main estimators and present methods to deal with endogeneity issues.

MATH-336: Randomization and causation

This course covers formal frameworks for causal inference. We focus on experimental designs, definitions of causal models, interpretation of causal parameters and estimation of causal effects.

Statistics

Statistics (from German: Statistik, "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and present

Experiment

An experiment is a procedure carried out to support or refute a hypothesis, or determine the efficacy or likelihood of something previously untried. Experiments provide insight into cause-and-effe

Propensity score matching

In the statistical analysis of observational data, propensity score matching (PSM) is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other interventi