A common element in all experiments is the deliberate manipulation of an assumed cause followed by an observation of the effects that follow. A quasi-experiment is an experiment that does not uses random assignment of participants to conditions.

An inus condition is an insufficient but non-redundant part of an unnecessary but sufficient condition. It is insufficient, because in itself it cannot be the cause, but it is also non-redundant as it adds something that is unique to the cause. It is an insufficient cause.

Most causal relationships are non-deterministic. They do not guarantee that an effect occur, as most causes are inus conditions, but they increase the probability that an effect will occur. To different degrees, all causal relationships are contextually dependent.

A counterfactual is something that is contrary to fact. An effect is the difference between what did happen and what would have happened. The counterfactual cannot be observed. Researchers try to approximate the counterfactual, but it is impossible to truly observe it.

Two central tasks of experimental design are creating a high-quality but imperfect source of counterfactual and understanding how this source differs from the experimental condition.

Creating a good source of counterfactual is problematic in quasi-experiments. There are two tools to attempt this:

1. Observe the same unit over time
2. Make the non-random control groups as similar as possible to the treatment group

A causal relationship exists if the cause preceded the effect (1), the cause was related to the effect (2) and there is no plausible alternative explanation for the effect other than the cause (3). Although quasi-experiments are flawed compared to experimental studies, they improve on correlational studies in two ways:

1. Quasi-experiments make sure the cause precedes the effect by first manipulating the presumed cause and then observing an outcome afterwards.
2. Quasi-experiments allows to control for some third-variable explanations. 

Campbell’s threats to valid causal inference contains a list of common group differences in a general system of threats to valid causal inference:

1. History
   Events occurring concurrently with treatment could cause worse performance.
2. Maturation
   Naturally occurring changes over time, not too be confused with treatment effects.
3. Selection
   Systematic differences over conditions in respondent characteristics.
4. Attrition
   A loss of participants can produce artificial effects if that loss is systematically correlated with conditions.
5. Instrumentation
   The instruments of measurement might differ or change over time.
6. Testing
   Exposure to a test can affect subsequent scores on a test.
7. Regression to the mean
   An extreme observation will be less extreme on the second observation.

Two flaws of falsification are that it requires a causal claim to be clear, complete and agreed upon in all its details and it requires observational procedures to perfectly reflect the theory that is being tested.