PPT-Strengthening Causal Inference in HIV Studies: Introduction and Practical Examples

HIV Studies Introduction and Practical Examples CAPS Methods Core Presentation April 18 2012 Starley Shade Sheri Lippman MiSuk Kang Dufour amp Carol Camlin Outline Answering causal questions common roadblocks in . 1093panmpr013 Causal Inference without Balance Checking Coarsened Exact Matching Stefano M Iacus Department of Economics Business and Statistics University of Milan Via Conservatorio 7 I20124 Mila The LIFT II Project. Funded by USAID Global Health Bureau’s Office of HIV/AIDS . Five-year project, through July 2018. Three core partners (FHI360, CARE and World Vision) and numerous resource organizations. Children’s outcomes and family background. Claire Crawford. Introduction. UK has relatively low intergenerational mobility. Correlation between parents and children’s income is relatively high. Intergenerational elasticity of 0.29 for those born in Britain in 1970 (. . Mapping . Strand – reflecting on professional practice. John Wardle, Centre for Science Education, Sheffield Hallam University. Mapping strand. Purpose. Produce a framework to encourage teachers to review and classify practical tasks. Enable:. Susan Athey, Stanford GSB. Based on joint work with Guido Imbens, Stefan Wager. References outside CS literature. Imbens and Rubin Causal Inference book (2015): synthesis of literature prior to big data/ML. Peter Spirtes. Carnegie Mellon University. With slides from Lizzie Silver. Outline. Biology. Data and Background Knowledge. Problems. Algorithms. Causal Graph. gene protein. mRNA mRNA . protein gene. – Bobby Wellins . (Jazz Line-up, 13/2/2011. ). “Best of all . is to . convey . the magnitude of the effect and the degree of certainty explicitly. .”. – . Pinker (2014. , p. 45. ). “Usually . Kenneth A. Frank . Guan Saw, UT San Antonio. AERA workshop April 4, 2014 (. AERA on-line video – cost is $95. ). Motivation . Statistical inferences are often challenged because of uncontrolled bias. There may be bias due to uncontrolled confounding . Austin Nichols (Abt) & Linden McBride (Cornell). July 27, 2017. Stata Conference. Baltimore, MD. Overview. Machine learning methods dominant for classification/prediction problems.. Prediction is useful for causal inference if one is trying to predict propensity scores (probability of treatment conditional on observables);. Alex . Dimakis. UT Austin. j. oint work with . Murat . Kocaoglu. , . Karthik. . Shanmugam. Sriram. . Vishwanath. , . Babak. . Hassibi. Overview. Discovering causal directions . Part 1: . Interventions. Sciences: QUICK EXAMPLES. #. konfoundit. Kenneth A. . Frank. Ran . Xu; Zixi . Chen. ; I-Chien Chen, Guan Saw. 2018. (. AERA on-line video – cost is . $105. ). Motivation . Statistical inferences are often challenged because of uncontrolled bias. There may be bias due to uncontrolled confounding . Sciences: QUICK EXAMPLES. #. konfoundit. Kenneth A. . Frank. Ran . Xu; Zixi . Chen. ; I-Chien Chen, Guan Saw. 2018. (. AERA on-line video – cost is . $105. ). Motivation . Statistical inferences are often challenged because of uncontrolled bias. There may be bias due to uncontrolled confounding . Alex . Dimakis. UT Austin. j. oint work with . Murat . Kocaoglu. , . Karthik. . Shanmugam. Sriram. . Vishwanath. , . Babak. . Hassibi. Overview. Discovering causal directions . Part 1: . Interventions. In the daily news and the scientific literature, we are faced with conflicting claims about the effects caused by some treatments, behaviors, and policies. A daily glass of wine prolongs life, or so we are told. Yet we are also told that alcohol can cause life-threatening cancer and that pregnant women should abstain from drinking. Some say that raising the minimum wage decreases inequality while others say it increases unemployment. Investigators once confidently claimed that hormone replacement therapy reduces the risk of heart disease but today investigators confidently claim it raises that risk. How should we study such questions?Observation and Experiment is an introduction to causal inference from one of the field\'s leading scholars. Using minimal mathematics and statistics, Paul Rosenbaum explains key concepts and methods through scientific examples that make complex ideas concrete and abstract principles accessible.Some causal questions can be studied in randomized trials in which coin flips assign individuals to treatments. But because randomized trials are not always practical or ethical, many causal questions are investigated in nonrandomized observational studies. To illustrate, Rosenbaum draws examples from clinical medicine, economics, public health, epidemiology, clinical psychology, and psychiatry. Readers gain an understanding of the design and interpretation of randomized trials, the ways they differ from observational studies, and the techniques used to remove, investigate, and appraise bias in observational studies. Observation and Experiment is a valuable resource for anyone with a serious interest in the empirical study of human health, behavior, and well-being.