Presentations

University of South Carolina | Columbia, SC, USA — Talk

02 Feb 2024

HOST: Research Center for Child Well-Being (RCCWB)

TITLE: Discovering Your Own Health Behavior Causal Effects Using Wearables and Apps

University of the Philippines | Diliman, Quezon City, Metro Manila, Philippines — Talk

09 Nov 2023

HOST: School of Statistics

TITLE: Using Wearables and Apps to Characterize Your Own Recurring Average Treatment Effects

JEDI Outreach Group Webinar | Online — Panel

16 Oct 2023

TITLE: Building Successful Mentor/Mentee Relationships in the Hybrid Work Era

StatFest 2023 | Cary, NC, USA — Session

23 Sep 2023

HOST: SAS Headquarters

TITLE: Stories of Leadership (Cultivating the Next Generation of Leaders)

International Society for Quality of Life Research (ISOQOL) 30th Annual Conference | Calgary, AB, Canada — Workshop

18 Oct 2023

Society of Behavioral Medicine (SBM) 42nd Annual Meeting | Virtual — Talk

12-16 Apr 2021

TITLE: Epidemiology-of-1: Causal Inference via Single-case Observational Design for Sleep and Physical Activity Wearables Data

ABSTRACT: Temporally rich single-subject health data have become increasingly available thanks to wearable devices, mobile apps, sensors, and implants. Many health caregivers and “self-trackers” want to use such information to help a specific person figure out how to change their behavior to achieve desired health outcomes. However, this requires an approach for discerning possible causes from correlations using that person’s own observational time series data. In this paper, we posit and estimate some plausible idiographic average treatment effects of sleep duration on physical activity. We use a recently developed causal inference framework based on n-of-1 randomized trials to analyze one year of the lead author’s Fitbit sleep duration and step count data. We then compare our findings to those of standard methods that do not account for confounding to show that causal inference is needed to make realistic recommendations for personal behavior change.

Pilipinx American Public Health Conference (PAPHC) | Virtual — Talk

13 Nov 2020

TITLE: Making it Count: Statistics and Data Science in Public Health

ABSTRACT: Understanding and shaping the health of populations requires both qualitative and quantitative scientific methods. Statistical concepts are used to structure and manage the uncertainty inherent in the scientific study of human life, behavior, and society. This workshop presents an overview of this quantitative discipline’s role in the field of public health—in biostatistics and epidemiology in particular.

Joint Statistical Meetings (JSM) 2019 | Denver, CO, USA — Talk + Poster

28 Jul 2019

TITLE: Person as Population: a Longitudinal View of Single-Subject Causal Inference for Analyzing Self-Tracked Health Data

Asian Institute of Management | Makati, NCR, Philippines — Talk

11 Oct 2017

HOST: Data Science Philippines

TITLE: Design Trumps Analysis: Drawing Causal Conclusions using Big Data

ABSTRACT: This talk provides a high-level, fairly non-technical introduction to causal discovery using big data; i.e., how to carefully draw causal conclusions from big data analyses. Two general, complementary approaches for causal discovery will briefly be illustrated in the context of big data analysis: 1.) mechanism-focused and structural approaches using causal graphs, and 2.) the effect-focused statistical framework of potential outcomes (emphasis on the latter).