HPS 0610 Statistics and Causal Reasoning
Spring 2004 – Syllabus

1. General Information

Administrative Course Website: http://courseweb.pitt.edu

Pay attention to this website – announcements and any schedule changes will be made here. This site does not contain the content for the course. The content can be found at:

Content Website: http://www.phil.cmu.edu/courses/csr

You will need to set up an account to be able to the assigned work for this class and to get credit for it. All issues relating to computer usage are covered in a separate document available on the course website above.

Instructor

Ingo Brigandt
Department of History and Philosophy of Science

Office:		 CL 901-M
Office hours:	 T, H 5-6 pm, and by appointment
Email:		 inb1@pitt.edu
Phone:		 412 624 3183

Class Meeting Times:

Section 1  (CRN 32473) 
Tuesday 6:00-6:50pm  LAWRN 105   (Worked examples and case studies)
Tuesday 7:00-7:50pm  LAWRN 104   (Extra help)

Section 2  (CRN 32501) 
Thursday 6:00-6:50pm  CL G13   (Worked examples and case studies)
Thursday 7:00-7:50pm  CL G13   (Extra help)

Books: There are no required texts. All course materials are available in the content website.

2. Overview

Do school vouchers really help inner city students become better educated? Do gun control laws really make society safer? This course examines how scientists reason about causal claims. It considers the scientific use of statistical data that informs our public policy debates.

The content of the course will consist of five units, each with several modules:

Unit 1. Causation
1.1  Causation: Preliminaries
1.2  Causation among Variables
1.3  Indeterministic Causation
1.4  Causal Graphs
1.5  Interventions

Unit 2. Association
2.1  Relative frequency
2.2  Conditional Relative Frequency
2.3  Independence and Association
2.4  Conditional Independence

Unit 3. Causation to Association
3.1  Causation vs. Association
3.2  Causation to Association
3.3  Causation to Conditional Association
3.4  D-separation

Unit 4. Association to Causation: Problems
4.1  Problems with Causal Discovery
4.2  Confounding (qualitatively)

Unit 5. Association to Causation: Strategies
5.1  Experiments

In the first unit we will examine what causal claims mean, how they involve manipulations and interventions, and how they can be represented with causal graphs. In the second unit, we examine the kind of data scientists collect, and cover the key relationship that scientist look for: association. In the third unit, we connect causation to association by showing how causal theories make predictions about association. In the fourth unit, we discuss the problems involved in moving the other way – from observed associations to causal theories. Finally, in the fifth unit, we discuss strategies for overcoming some of these problems.

3. Course Format

Unlike most courses, this course involves no lectures (at least, not as normally conceived). Except for exams (2 midterms and a final), all meetings with the instructor will be in recitation format. You are expected to work through the modules online before going to the recitation in which they are scheduled to be covered (see schedule below). This is very important. It will be very difficult to pass this class without doing the required preparatory work. Two recitations will be offered each week: 

•	Recitation 1:  Worked examples and case studies (6:00-6:50pm)
•	Recitation 2:  Extra help (7:00-7:50pm)

In Recitation 1, the instructor will go over problems, work through examples, exercises and quiz questions that deal with the concepts and skills presented in the modules. We will also work through case studies assigned in the modules or as extra assignments (either in class or for homework) to help you apply the concepts learned to real life. 

Recitation 2 will be reserved for dealing with any other questions or issues that arise. There will be ample time for students to have their specific questions answered, or to work through problems on a one-to one basis with the instructor (which may often be necessary).

Attendance at Recitation 1 is mandatory. You should bring your own questions on each module to the recitation scheduled to cover it. They can range from typos that confused you to deep philosophical confusions about the assumptions underlying the module. We encourage you to ask the instructor to go over these questions. You need to take responsibility for engaging the material before class and coming prepared to discuss what you have covered. The instructor will also be available to answer any questions that come up either in office hours or via email.

4. Tentative Schedule

The dates below indicate when a particular module will be covered in recitation. To get full credit for finishing a module, you need to have finished the module, which requires taking the quiz at the end of the module before it is covered in recitation. You will have two further attempts to take the quiz after the recitation if you wish to try to improve your score. If you take a quiz a second or third time, you will be tested on the same concepts, but the questions will be different. Each quiz is assembled by random selection from a large pool of questions.

Unit 1. Causation

Week 1:   	Tuesday Jan 6th or Thursday Jan 8th
Introductory Class

Week 2:   	Tuesday Jan 13th or Thursday Jan 15th
1.1  Causation: Preliminaries

Week 3:   	Tuesday Jan 20th or Thursday Jan 22nd
1.2  Causation among Variables

Week 4:   	Tuesday Jan 27th or Thursday Jan 29th
1.3  Indeterministic Causation

Week 5:   	Tuesday Feb 3rd or Thursday Feb 5th
1.4  Causal Graphs
1.5  Interventions

Week 6:   	Tuesday Feb 10th or Thursday Feb 12th
Midterm 1

Unit 2. Association

Week 7:   	Tuesday Feb 17th or Thursday Feb 19th
2.1  Relative Frequency
2.2  Conditional Relative Frequency

Week 8:   	Tuesday Feb 24th or Thursday Feb 26th
2.3  Independence and Association
2.4  Conditional Independence

Unit 3. Causation to Association

Week 9:   	Tuesday Mar 2nd or Thursday Mar 4th 
3.1  Causation vs. Association
3.2  Causation to Association

Week 10:   	Tuesday Mar 9th or Thursday Mar 11th
No class - Spring Break

Week 11:   	Tuesday Mar 16th or Thursday Mar 18th
3.3  Causation to Conditional Association
Review
Week 12:   	
Tuesday Mar 23rd or Thursday Mar 25th
Midterm 2

Week 13:   	Tuesday Mar 30th or Thursday Apr 1st
3.4  D-separation

Unit 4. Association to Causation: Problems

Week 14:   	Tuesday Apr 6th or Thursday Apr 8th
4.1  Problems with Causal Discovery
4.2  Confounding (qualitatively)

Unit 5. Association to Causation: Strategies

Week 15:   	Tuesday Apr 13th or Thursday Apr 15th
16. Experiments 
Review

Week 16:   	
Review Session: Time and Place TBA
Final Exam: Time and Place TBA

5. Grading

Your grade will be based on the following four components:

1.	End-of–module quizzes	(1% each, 16% total)
•	quizzes are pass/fail 		(60% or above required to pass)
•	quizzes must be attempted by the due date to receive full credit

2.	Recitation attendance 		(1% each, 10% total)

3.	Media case study		(10%)
•	in April you need to choose a short case study that makes causal claims from the media, web, etc. and examine it based on what you have learned in class
•	deadline and more details will be given out later in the term

4.	Exams				(64% total)
•	Midterms, 20% each
•	Final Exam, 24%