Nicolás Della Penna

Response to Comment on "Estimating the reproducibility of psychological science"

Gilbert et al. conclude that evidence from the Open Science Collaboration’s Reproducibility Project: Psychology indicates high reproducibility, given the study methodology. Their very optimistic assessment is limited by statistical misconceptions and by causal inferences from selectively interpreted, correlational data. Using the Reproducibility Project: Psychology data, both optimistic and pessimistic conclusions about reproducibility are possible, and neither are yet warranted.

New objective functions for social collaborative filtering

This paper examines the problem of social collaborative filtering (CF) to recommend items of interest to users in a social network setting. Unlike standard CF algorithms using relatively simple user and item features, recommendation in social networks poses the more complex problem of learning user preferences from a rich and complex set of user profile and interaction information. Many existing social CF methods have extended traditional CF matrix factorization, but have overlooked important aspects germane to the social setting. We propose a unified framework for social CF matrix factorization by introducing novel objective functions for training. Our new objective functions have three key features that address main drawbacks of existing approaches: (a) we fully exploit feature-based user similarity, (b) we permit direct learning of user-to-user information diffusion, and (c) we leverage co-preference (dis)agreement between two users to learn restricted areas of common interest. We evaluate these new social CF objectives, comparing them to each other and to a variety of (social) CF baselines, and analyze user behavior on live user trials in a custom-developed Facebook App involving data collected over five months from over 100 App users and their 37,000+ friends.

Instrumental Variable Analysis of Electronic Health Records

Sources of variation in treatments received that are exogenous to patients can be used to estimate causal effects from observational data. We present an example of this methodology that estimates the effect of critically ill patients being cared for in “non-target ICUs” due to capacity constraints—a process known as boarding.