Identification Strategy and Statistical Inference Quiz

<p>By following the arrows pointing towards the outcome variable from the cause variable.</p> Signup and view all the answers

<p>The causal effect is not identified.</p> Signup and view all the answers

<p>Mediator influenced by the treatment with a causal effect on Y</p> Signup and view all the answers

<p>Potential omitted variable bias affecting the causal relationship</p> Signup and view all the answers

<p>Chain, fork, and collider structures</p> Signup and view all the answers

<p>Collider impacted by both D and Y</p> Signup and view all the answers

<p>To differentiate between direct and indirect effects of treatments</p> Signup and view all the answers

<p>It makes potential outcomes independent of treatment by removing arrows pointing to the target variable.</p> Signup and view all the answers

<p>It introduces a spurious association between the causes.</p> Signup and view all the answers

<p>The Back-Door criterion</p> Signup and view all the answers

<p>When the middle node M is in the conditioning set of variables C.</p> Signup and view all the answers

<p>Leads to an increase in omitted variable bias.</p> Signup and view all the answers

<p>Variables such as outcome, treatment, observed and unobserved factors</p> Signup and view all the answers

<p>Non-causal path</p> Signup and view all the answers

<p>Omitted variable bias can lead to unobserved confounding in DAGs</p> Signup and view all the answers

<p>To properly identify the causal effect</p> Signup and view all the answers

<p>The causal effect remains unidentified</p> Signup and view all the answers

<p>By conditioning on the variable at the start of the path</p> Signup and view all the answers

<p>By only conditioning on one variable in each back-door path</p> Signup and view all the answers

<p>The path remains open</p> Signup and view all the answers

<p>Because X1 closes the back-door paths</p> Signup and view all the answers

<p>Only identifies the direct effect</p> Signup and view all the answers

<p>To visualize the relationships between variables in a study</p> Signup and view all the answers

<p>It inflates the magnitude of the causal effect</p> Signup and view all the answers

<p>To eliminate confounding variables from affecting the causal effect estimation</p> Signup and view all the answers

<p>The unobserved outcome of interest</p> Signup and view all the answers

<p>Indirect effects represent the influence of mediators on a causal pathway</p> Signup and view all the answers

<p>All causal paths are present in the DAG.</p> Signup and view all the answers

<p>Values of variables are set by hypothetical interventions in the under-intervention regime.</p> Signup and view all the answers

<p>P [Y |do(D = 1)] and P [Y |do(D = 0)]</p> Signup and view all the answers

<p>Average Treatment Effect</p> Signup and view all the answers

<p>It represents a hypothetical intervention.</p> Signup and view all the answers

<p>They define all causal effects.</p> Signup and view all the answers

<p>To represent well-defined causal states.</p> Signup and view all the answers

<p>Common causes of the treatment and outcome variables.</p> Signup and view all the answers

<p>A situation where treatment effects are overestimated due to confounding.</p> Signup and view all the answers

<p>The association between treatment and outcome mediated by intermediate variables.</p> Signup and view all the answers

<p>To establish a causal effect between treatment D and outcome Y by eliminating confounding variables.</p> Signup and view all the answers

<p>Counterfactuals represent what would have happened under different conditions, helping to quantify causal effects in DAGs.</p> Signup and view all the answers

<p>Open non-causal paths introduce bias and make it challenging to accurately estimate the causal effect.</p> Signup and view all the answers

<p>X1 is considered a bad control when it lies on the causal path from treatment D to outcome Y.</p> Signup and view all the answers

<p>Counterfactual Y0 represents the outcome Y under the scenario where treatment D is absent or not applied.</p> Signup and view all the answers

<p>To establish causal relationships between variables and identify the causal effect of a treatment on an outcome.</p> Signup and view all the answers

<p>Controlling for confounders helps in isolating the effect of the treatment on the outcome, ensuring that the observed association is not confounded by other variables.</p> Signup and view all the answers

<p>Counterfactuals and potential outcomes allow researchers to compare what would have happened under different treatment conditions, helping to quantify causal effects.</p> Signup and view all the answers

<p>ATE helps in quantifying the average difference in outcomes between treatment and control groups, providing insights into the causal impact of the treatment.</p> Signup and view all the answers

<p>The observational problem arises when causal relationships are inferred from observational data without experimental manipulation, leading to potential biases and confounding.</p> Signup and view all the answers

<p>The observed outcome is what is actually seen in the data, while the potential outcome is the outcome that would have been observed under a different treatment condition.</p> Signup and view all the answers

<p>ATE is the difference between the average outcome if everyone received the treatment and the average outcome if no one received the treatment. It helps in understanding the overall impact of a treatment.</p> Signup and view all the answers

<p>The selection effect occurs when individuals self-select into a treatment based on certain characteristics, leading to biased estimates of treatment effects. It can confound the true causal effect of the treatment.</p> Signup and view all the answers

<p>Random assignment helps in making the selection bias zero, ensuring that treatment assignment is not based on confounding variables. This allows for a well-defined average potential impact.</p> Signup and view all the answers

<p>The counterfactual Y0 represents what would have happened to an individual if they were not exposed to the treatment. It serves as a benchmark for comparing the actual outcome with the potential outcome under a different treatment condition.</p> Signup and view all the answers

<p>Causal graphs help in illustrating and analyzing causal models by depicting variables as nodes and direct causal effects as arrows.</p> Signup and view all the answers

<p>A path in a DAG is an acyclic sequence of adjacent nodes, where causal paths show arrows pointing away from the cause and into the outcome.</p> Signup and view all the answers

<p>As long as there are open non-causal paths, the causal effect is not identified.</p> Signup and view all the answers

<p>Controlling for confounders in a DAG involves holding certain variables fixed to close open non-causal paths.</p> Signup and view all the answers

<p>Closing open non-causal paths is essential for identifying the causal effect between the treatment and the outcome.</p> Signup and view all the answers

<p>The non-causal path D → X1 ← X2 → Y is opened, and regression of Y on D and X1 does not identify the direct effect.</p> Signup and view all the answers

<p>The back-door paths are closed by conditioning on certain variables, allowing the causal effect of D on Y to be identified.</p> Signup and view all the answers

<p>X1 is considered a bad control because conditioning on it can open non-causal paths and fail to identify the direct effect of D on Y.</p> Signup and view all the answers

<p>The back-door path is closed by conditioning on X1, allowing the identification of the sum of direct and indirect effects.</p> Signup and view all the answers

<p>Regression of Y on D identifies the sum of direct and indirect effects, as X1 being a collider closes the path.</p> Signup and view all the answers

<p>Back-door paths are paths between variables that connect the treatment and outcome, and they are blocked by conditioning on variables that are descendants of the common causes but not part of the causal pathway.</p> Signup and view all the answers

<p>The Average Treatment Effect (ATE) is the average difference in outcomes between the treatment group and the control group. It is estimated by comparing the outcomes of the treated and control groups after accounting for potential confounders.</p> Signup and view all the answers

<p>The Selection effect refers to the bias introduced when the treatment assignment is related to the potential outcomes. It can lead to overestimation or underestimation of the true causal effect.</p> Signup and view all the answers

<p>Counterfactuals refer to the unobserved outcome that would have occurred if a different treatment had been received. Potential Outcomes represent the different outcomes that could result from different treatment options.</p> Signup and view all the answers

<p>The Causal effect represents the impact of a treatment or exposure on the outcome of interest. Estimating the causal effect helps in making informed decisions about interventions and policies based on observational data.</p> Signup and view all the answers

<p>Counterfactuals help in understanding what would have happened under different conditions.</p> Signup and view all the answers

<p>Potential outcomes help in comparing the outcome under different treatment conditions.</p> Signup and view all the answers

<p>Selection effect pertains to the differences in outcomes due to the selection of certain treatments or groups.</p> Signup and view all the answers

<p>Average Treatment Effect (ATE) is the average difference in outcomes between the treatment group and the control group.</p> Signup and view all the answers

<p>Observational problem arises when treatment assignment is non-random, leading to biased estimates of causal effects.</p> Signup and view all the answers

<p>Causal effect refers to the impact of a treatment or intervention on the outcome of interest.</p> Signup and view all the answers

<p>Randomized experiments help in establishing causal relationships by ensuring treatment assignment is not influenced by any confounding variables.</p> Signup and view all the answers

<p>Matching helps in creating comparable treatment and control groups by selecting similar units based on observed characteristics.</p> Signup and view all the answers

<p>Instrumental variables help in dealing with endogeneity by providing a source of exogenous variation to identify causal effects.</p> Signup and view all the answers

<p>Regression discontinuity allows for causal effects to be estimated based on the presence of a cutoff point.</p> Signup and view all the answers