Online Causal Inference Seminar

Free and open to the public
All seminars are on Tuesdays at 8:30 am PT (11:30 am ET / 4:30 pm London / 5:30 pm Berlin).

• Tuesday, July 20, 2021 (Joint Seminar) [Link to join] (ID: 995 8569 5110, Password: 007080)
  - Speaker #1: Fiammetta Menchetti (Universita degli Studi di Firenzi)
  - Title: Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach
  - Abstract: The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes. In recent years, several methods have been developed under the RCM to estimate causal effects in time series settings. None of these makes use of ARIMA models, which are instead very common in the econometrics literature. We propose a novel approach, C-ARIMA, to define and estimate the causal effect of an intervention in a time series setting under the RCM. We first formalize the assumptions enabling the definition, the estimation and the attribution of the effect to the intervention. In the empirical application, we use C-ARIMA to assess the causal effect of a permanent price reduction on supermarket sales.
  
  - Speaker #2: Armeen Taeb (ETH Zürich)
  - Title: Perturbations and causality in Gaussian latent variable models
  - Abstract: With observational data alone, causal inference is a challenging problem. The task becomes easier when having access to data from perturbing the underlying system, even when the perturbations are happening in an unspecific and non-randomized way. We provide results that enable causal discovery in this setting, and also allow for the presence of latent variables. In particular, we examine a perturbation model for interventional data over a collection of Gaussian variables. Given access to data arising from perturbations, we will introduce a regularized maximum-likelihood framework that determines the class of equally representative DAGs, and uniquely identifies the underlying causal structure under sufficiently heterogeneous data. We illustrate the effectiveness of our framework on synthetic data as well as real data involving California reservoirs.