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Summary
You've probably heard the old adage "correlation does not imply causation" but at some point we've got to say that drinking boiling hot tea and burning our tongues are more than just "strongly correlated". Enter Causal Inference, a collection of techniques for reasoning about the relationship between effects that can be measured and causes that can be identified. It helps us bridge between what we hear when we talk directly to users and what we observe about their behavior at scale and over time. This talk will introduce some of the key techniques in causal inference and how they can be used by UX Researchers and Designers to understand potential users, current users, and the products we might build.
Key Insights
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Causal inference combines quantitative data analysis with qualitative insights to understand user behavior better.
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Establishing a causal relationship often begins with qualitative reasoning and hypotheses about causal mechanisms.
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Quantitative analysis is critical but must be supported by a solid qualitative understanding of the context.
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True experiments require random assignment and controlled conditions to determine causal effects accurately.
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Confounders can obscure the understanding of causal relationships, requiring careful consideration in study design.
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Causal inference techniques include regression analysis, matching, and synthetic controls, each applicable in different contexts.
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Understanding the relationships between variables is key for effective product and service design.
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Causal reasoning helps designers articulate the impact of their work on user behavior and outcomes.
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It is essential to measure the effects of interventions on different user groups to understand personalized design needs.
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Effective collaboration between designers and data analysts is crucial for interpreting data meaningfully.
Notable Quotes
"There's a double bridging going on here, bringing quantitative thinking to design problems and design thinking to quantitative problems."
"Causal inference is reasoning about relationships and identifying causes and effects."
"When you do causal inference, you very much bridge between quantitative and qualitative thinking."
"Causal inference isn't really a field unto itself. It's borrowing from epidemiology, economics, and data science."
"Qualitative research helps set context and poses questions that can't be answered by numbers alone."
"It's important to understand your users and the context of the interventions being studied."
"We need to think about the causal mechanisms at play when analyzing user behavior."
"The replication crisis in social science highlights the need for rigorous experimental design."
"As designers, we should ask questions about how we can measure the effectiveness of our designs."
"Causal inference is another tool to frame inquiry, which can enhance design processes."
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