Calling all data scientists and statisticians. Let's get technical for the next SAS Bowl. Causal analysis is a field of statistics and experimental design that involves identifying and understanding the causes of an event. The goal of a causal analysis is to quantify the causal link between treatment and the outcome of interest. SAS has multiple procedures and tools to assist with causal analysis and we'll explore more about the techniques and benefits in December's SAS Bowl.
Game Details
The SAS Support Community trivia event, SAS Bowl XLVI, Causal Analysis is scheduled for Wednesday, December 18, at 10 AM ET.
Register for the event and receive an invite to a Microsoft Teams meeting and a calendar event.
On game day, join the Teams meeting and access the game here.
More on Causal Analysis
Here are some things causal analysis can help with:
Identifying causes Causal analysis can help identify the causes of an event, both individual and organizational. Distinguishing causality Causal analysis can help distinguish between direct and indirect causality. Assessing research Causal analysis can help assess the assumptions, merits, limitations, and key issues in research. Drawing conclusions Causal analysis can help draw conclusions about whether a treatment caused an effect. Predicting outcomes Causal analysis can help businesses predict outcomes like product demand and inventory shrinkage.
And Here are some steps for performing a causal analysis:
Identify the key challenge or setback. Determine the causes and effects of the key challenge.
Below are portal-related resources we'll use to create questions for the game.
Causal Analysis Using SAS Statistics Procedures - SAS Global Forum talk/paper
Causal Effect Estimands: Interpretation, Identification, and Computation - SAS Global Forum paper; SAS Global Forum video
SAS Causal Analysis Documentation - SAS documentation on causal analysis
Tools for Assessing a Propensity Score Model in SAS/STAT - SAS video on SAS/STAT procedures for causal analysis
SAS Bowl and event details
For those who may be new to the SAS Bowl, you can find game history and specifics in this Community memo. There you'll also find links to previous events, which include recordings.
Register for the event and receive an invite with game details and a Teams meeting link. On game day, Join the TEAMS meeting to play, and show off your SAS and worldly knowledge while competing for bragging rights and SAS Community game gear.
Date: Thursday, January 23, 2025 Time: Noon - 1:00 PM ET Place: Online Cost: Free! However, please register by Tuesday, January 21 in order to receive the webinar link. Webinar information will be sent out on Wednesday, January 22. Registration will open soon at https://www.misug.org/
Agenda
SAS® PROC GEOCODE By Example: A Case Study - Louise Hadden, CORMAC
Reporting on missing and/or non-response data is of paramount importance when working with longitudinal surveillance, laboratory, and medical record data. For a CDC surveillance project with thousands of variables and weekly deliveries, an efficient and comprehensive assessment of missing values was required. PROC FREQ with the NLEVELS option, PROC REPORT and traffic-lighting, and PROC UNIVARIATE OUTTABLES can produce an effective, data-driven visualization. Data processing was performed using SAS 9.4 M7. This presentation is suitable for all skill levels.
Looking for the Missing(ness) Piece - Louise Hadden, CORMAC
Numerous international and domestic governments provide free public access to downloadable databases containing health data. One example is the Centers for Medicare and Medicaid Services' Care Compare data, which contains address information for providers. This paper and presentation will describe the process of downloading data and creating an analytic data base; running SAS®'s PROC GEOCODE (part of Base SAS®) using Tiger street address level data to obtain latitude and longitude at a finer level than zip code; modeling the data points using SAS/Stat, and finally using PROC SGMAP (part of Base SAS®) with annotation to create a visualization of a proximity analysis. Data processing was accomplished using BASE SAS® ( SAS 9.4 M7). This presentation is suitable for all skill levels. Louise Hadden presented at her first SAS conference in 1996 and has never looked back, presenting at multiple conferences across the continent over the years. She has given over 200 SAS user group presentations! She supports file building and analytic programming for CORMAC, most frequently on contracts with government agencies such as CMS and CDC, and specializes in reporting and data visualization. In her spare time, she reads voraciously, volunteers with the MSPCA, donates platelets with the Red Cross (up to 35 gallons!), and dabbles with photography.
